OSCR

Frequency and prognostic outcomes of emergency diagnosis in 13 non-neoplastic conditions in England: A population-based cohort study using linked electronic health records of 1.7 million patients.

Code ↔ Paper

7 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 7 matches
  1. [1] § Methods › Study population ↔ C2_run_analyses.Rmd, lines 584–633 · score 0.81 · axial spondyloarthritis, rheumatoid arthritis, multiple sclerosis, Lyme disease, coeliac disease, MS
  2. [2] § Results › Association with prognosis ↔ C2_run_analyses.Rmd, lines 584–633 · score 0.68 · brain tumours, colon cancer, Lyme disease, coeliac disease, MS, tuberculosis
  3. [3] § Results › Frequency and characteristics of emergency diagnosis ↔ C2_run_analyses.Rmd, lines 635–699 · score 0.66 · 31–35, 66–70, pancreatic cancer, Lyme disease, schizophrenia, age
  4. [4] § Results › Association with prognosis ↔ C3_report_EDs.Rmd, lines 835–910 · score 0.63 · brain tumours, colon cancer, year mortality, emergency diagnosis, tuberculosis, schizophrenia
  5. [5] § Results › Association with prognosis ↔ C2_run_analyses.Rmd, lines 701–759 · score 0.56 · axial spondyloarthritis, multiple sclerosis, coeliac disease, Parkinson, SBE, PCOS
  6. [6] § Results › Frequency and characteristics of emergency diagnosis ↔ C3_report_EDs.Rmd, lines 216–268 · score 0.54 · 31–35, 66–70, deprivation, pancreatic, schizophrenia, Lyme
  7. [7] § Methods › Statistical analyses ↔ C3_report_EDs.Rmd, lines 216–268 · score 0.50 · IMD twentile, 81–90, age, diagnosis

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

R Markdown · 765 lines · 33 KB · MIT · 4 matches

  1. ---
  2. title: "Emergency diagnoses - basic frequency and analyses"
  3. author: "Emma Whitfield"
  4. date: "`r Sys.Date()`"
  5. output: word_document
  6. ---
  8. ```{r setup, include=FALSE}
  9. knitr::opts_chunk$set(echo = TRUE)
  10. library(tidyverse)
  11. library(flextable)
  12. library(gtsummary)
  13. library(lubridate)
  14. library(RMySQL)
  15. library(glue)
  16. library(lmtest)
  17. library(glmmTMB)
  18. library(boot)
  19. source('A0_global_vars_R.R')
  21. sensitivity_analysis <- F # to run sensitivity analysis 1 set to TRUE and offset to FALSE
  22. offset <- F # to run sensitivity analysis 2 set to TRUE and sensitivity_analysis to FALSE
  23. exclude_covid <- F # to exclude potential covid bias on prognosis set to TRUE
  24. td <- today()
  26. results_filepath <- glue("{results_filepath}{td}")
  27. if (!file.exists(results_filepath)) {
  28. dir.create(file.path(results_filepath))
  29. }
  31. if (sensitivity_analysis) {
  32. results_filepath <- glue("{results_filepath}/sensitivity_analysis")
  33. if (!file.exists(results_filepath)) {
  34. dir.create(file.path(results_filepath))
  35. }
  36. }
  38. if (offset) {
  39. results_filepath <- glue("{results_filepath}/offset")
  40. if (!file.exists(results_filepath)) {
  41. dir.create(file.path(results_filepath))
  42. }
  43. }
  45. if (exclude_covid) {
  46. results_filepath <- glue("{results_filepath}/exclude_covid")
  47. if (!file.exists(results_filepath)) {
  48. dir.create(file.path(results_filepath))
  49. }
  50. }
  52. if (sensitivity_analysis) {
  53. ED_defn <- 'emergency hospital admission or A&E attendance'
  54. } else {
  55. ED_defn <- 'emergency hospital admission'
  56. }
  57. ```
  59. ```{r display, echo=FALSE, fig.align = "center", warning = FALSE, message = FALSE, results = "asis", fig.width = 16, fig.height = 12}
  60. if (sensitivity_analysis) {
  61. cat('# Sensitivity analysis\n')
  62. }
  64. if (offset) {
  65. cat('# Offset death\n')
  66. }
  68. if (exclude_covid) {
  69. cat('# Exclude 2019 diagnoses\n')
  70. }
  72. # 1. Useful functions ----
  73. extract_EDs <- function(pln, condition) {
  74. db <- dbConnect(
  75. MySQL(),
  76. host = host,
  77. user = user,
  78. password = password,
  79. dbname = dbname,
  80. port = port
  81. )
  82. # extract patients and followup
  83. pat_query <- dbSendQuery(conn = db,
  84. statement = glue('select * from {sql_schema}.{pln}_{condition}_patients;'))
  85. patients <- fetch(pat_query, n = -1)
  86. dbClearResult(pat_query)
  88. fup_query <- dbSendQuery(conn = db,
  89. statement = glue('select * from {sql_schema}.{pln}_{condition}_admissions;'))
  90. admit <- fetch(fup_query, n = -1)
  91. dbClearResult(fup_query)
  93. if (sensitivity_analysis) {
  94. # also get attendances
  95. fup_query <- dbSendQuery(conn = db,
  96. statement = glue('select * from {sql_schema}.{pln}_{condition}_attends;'))
  97. attend <- fetch(fup_query, n = -1)
  98. dbClearResult(fup_query)
  99. }
  101. launders_query <- dbSendQuery(conn = db,
  102. statement = glue('select * from {sql_schema}.{pln}_{condition}_launders;'))
  103. launders <- fetch(launders_query, n = -1)
  104. dbClearResult(launders_query)
  106. death_query <- dbSendQuery(conn = db,
  107. statement = glue('select * from {sql_schema}.{pln}_{condition}_death;'))
  108. death <- fetch(death_query, n = -1)
  109. dbClearResult(death_query)
  111. dbDisconnect(db)
  112. rm(list = c("pat_query", "fup_query", "launders_query", "death_query"))
  114. patients <- patients %>%
  115. mutate(diagdate = ymd(diagdate),
  116. startdate = ymd(startdate),
  117. enddate = ymd(enddate),
  118. deathdate = ymd(deathdate)) %>%
  119. mutate(imd = as.numeric(imd))
  121. # restrict to only patients diagnosed in relevant periods if needed
  122. if (sensitivity_analysis) {
  123. patients <- patients %>%
  124. filter(diagdate >= ymd('2007-05-01'))
  125. } else if (exclude_covid) {
  126. patients <- patients %>%
  127. filter(diagdate < ymd('2019-01-01'))
  128. }
  130. # make sure there are no extra patients
  131. cases <- unique(patients$patid)
  132. admit <- admit %>%
  133. filter(patid %in% cases)
  134. launders <- launders %>%
  135. filter(patid %in% cases)
  136. death <- death %>%
  137. filter(patid %in% cases)
  139. launders <- launders %>%
  140. mutate(recorddate = ymd(recorddate))
  142. admit <- admit %>%
  143. mutate(admidate = ymd(admidate)) %>%
  144. select(-description)
  146. # add time to diagnosis
  147. admit_ttd <- admit %>%
  148. distinct(patid, admidate, .keep_all = T) %>%
  149. left_join(patients %>% select(patid, diagdate, yob) %>% distinct(), by = join_by(patid)) %>%
  150. mutate(ttd = as.numeric(difftime(diagdate, admidate, units = "days")))
  152. # get emergency event closest to diagnosis for each patient
  153. latest_admit <- admit_ttd %>%
  154. group_by(patid) %>%
  155. summarise(earliest_ttd = min(ttd)) %>%
  156. filter(earliest_ttd <= 30)
  158. # identify ED patients
  159. if (sensitivity_analysis) {
  160. attend <- attend %>%
  161. filter(patid %in% cases)
  163. attend <- attend %>%
  164. mutate(arrivaldate = ymd(arrivaldate)) %>%
  165. select(-aepatgroup)
  167. attend_ttd <- attend %>%
  168. distinct(patid, arrivaldate, .keep_all = T) %>%
  169. left_join(patients %>% select(patid, diagdate, yob) %>% distinct(), by = join_by(patid)) %>%
  170. mutate(ttd = as.numeric(difftime(diagdate, arrivaldate, units = "days")))
  172. latest_attend <- attend_ttd %>%
  173. group_by(patid) %>%
  174. summarise(earliest_ttd = min(ttd)) %>%
  175. filter(earliest_ttd <= 30)
  177. patients_admit <- patients %>%
  178. mutate(ED = patid %in% c(latest_admit$patid, latest_attend$patid))
  179. } else {
  180. patients_admit <- patients %>%
  181. mutate(ED = patid %in% latest_admit$patid)
  182. }
  184. # get comorbidity score at six months before diagnosis date
  185. launders <- launders %>%
  186. filter(ttd >= 180) %>%
  187. group_by(patid) %>%
  188. summarise(comorb_score = n_distinct(comorbidity)) %>%
  189. select(patid, comorb_score)
  191. patients_admit <- patients_admit %>%
  192. left_join(launders, by = join_by(patid)) %>%
  193. mutate(comorb_score = replace_na(comorb_score, 0))
  195. patients_admit <- patients_admit %>%
  196. mutate(diagyear = year(diagdate),
  197. gender = case_when(gender == 1 ~ 'Men',
  198. gender == 2 ~ 'Women',
  199. gender == 3 ~ 'Indeterminate',
  200. gender == 4 ~ 'Unknown',
  201. gender == 0 ~ 'Data not entered',
  202. T ~ as.character(gender)),
  203. diagage = diagyear - yob,
  204. datasource = case_when(str_detect(datasource, 'ons') ~ 'ons',
  205. T ~ datasource)) %>%
  206. select(patid, ED, diagyear, datasource, diagage, gender, comorb_score, imd, diagdate, deathdate)
  208. # append prognosis
  209. patients_admit <- patients_admit %>%
  210. left_join(death, by = join_by(patid))
  212. return(patients_admit)
  213. }
  215. append_hospitalisations <- function(patients_admit, pln, condition) {
  216. db <- dbConnect(
  217. MySQL(),
  218. host = host,
  219. user = user,
  220. password = password,
  221. dbname = dbname,
  222. port = port
  223. )
  225. hosp_query <- dbSendQuery(conn = db,
  226. statement = glue('select * from {sql_schema}.{pln}_{condition}_hosp;'))
  228. hosp_records <- fetch(hosp_query, n = -1)
  229. dbClearResult(hosp_query)
  231. dbDisconnect(db)
  232. rm(hosp_query)
  234. # make sure no extra patients
  235. cases <- unique(patients_admit$patid)
  236. hosp_records <- hosp_records %>%
  237. filter(patid %in% cases)
  239. # prepare hospital counts
  240. # make sure not counting any night twice
  241. hosp_records <- hosp_records %>%
  242. mutate(diagdate = ymd(diagdate),
  243. admidate = ymd(admidate),
  244. discharged = ymd(discharged)) %>%
  245. group_by(patid) %>%
  246. arrange(desc(admidate)) %>%
  247. mutate(next_admission = lag(admidate)) %>%
  248. ungroup() %>%
  249. mutate(admidate = if_else(admidate <= diagdate, diagdate + days(1), admidate),
  250. discharged = if_else(!is.na(next_admission) & (discharged > next_admission), next_admission, discharged),
  251. discharged = if_else(discharged > diagdate + days(365), diagdate + days(365), discharged))
  253. # remove stays that start and end on same day - counting number of nights stayed
  254. hosp_records <- hosp_records %>%
  255. filter(admidate < discharged) %>%
  256. mutate(tih = as.integer(difftime(discharged, admidate, units = "days")))
  258. hosp_summary <- hosp_records %>%
  259. group_by(patid) %>%
  260. summarise(total_tih = sum(tih),
  261. n_admissions = n())
  263. # now add to patients_admit
  264. patients_admit <- patients_admit %>%
  265. left_join(hosp_summary, by = join_by(patid)) %>%
  266. mutate(total_tih = replace_na(total_tih, 0),
  267. n_admissions = replace_na(n_admissions, 0))
  269. return(patients_admit)
  270. }
  272. assess_prognosis <- function(patients_admit, pln, condition) {
  273. # restrict to patients who didn't die on diagnosis date
  274. no_death <- patients_admit %>%
  275. filter((diagdate != deathdate) | is.na(deathdate),
  276. !is.na(imd)) %>%
  277. mutate(datasource = as.factor(datasource),
  278. diagyear = diagyear - 1999)
  280. no_death <- no_death %>%
  281. mutate(death.bin = (!is.na(death)) & (death > 0) & (death <= 365),
  282. death_offset = ifelse(death.bin, death, 365))
  284. # function to bootstrap excess mortality for confidence intervals
  285. excess_mort <- function(data, indices) {
  286. d <- data[indices,]
  288. obs_temp <- d %>%
  289. group_by(ED) %>%
  290. summarise(n.pats = n(),
  291. n.death = sum(death.bin)) %>%
  292. mutate(prop.death = 100*n.death/n.pats)
  294. return(obs_temp$prop.death[obs_temp$ED] - obs_temp$prop.death[!obs_temp$ED])
  295. }
  297. # if main analysis estimate the excess mortality in ED vs non-ED patients
  298. if (!(offset | sensitivity_analysis | exclude_covid)) {
  299. excess_mort_res <- boot(data = no_death, statistic = excess_mort, R = 1000)
  300. excess_mort_ci <- boot.ci(excess_mort_res, type = "norm")
  301. }
  303. # function to bootstrap excess time in hospital
  304. excess_tih <- function(data, indices) {
  305. d <- data[indices,]
  307. obs_temp <- d %>%
  308. group_by(ED) %>%
  309. summarise(mean.time = mean(total_tih))
  311. return(obs_temp$mean.time[obs_temp$ED] - obs_temp$mean.time[!obs_temp$ED])
  312. }
  314. # if main analysis estimate the excess tih in ED vs non-ED patients
  315. if (!(offset | sensitivity_analysis | exclude_covid)) {
  316. excess_tih_res <- boot(data = no_death, statistic = excess_tih, R = 1000)
  317. excess_tih_ci <- boot.ci(excess_tih_res, type = "norm")
  318. }
  320. obs <- no_death %>%
  321. group_by(ED) %>%
  322. summarise(n.pats = n(),
  323. n.death = sum(death.bin),
  324. n.admitted = sum(n_admissions > 0),
  325. mean.admit = mean(n_admissions),
  326. mean.time = mean(total_tih)) %>%
  327. mutate(prop.death = 100*n.death/n.pats,
  328. prop.admitted = 100*n.admitted/n.pats)
  330. # calculate by diagyr but suppress anything for fewer than 10 patients
  331. obs_diagyr <- no_death %>%
  332. group_by(ED, diagyear) %>%
  333. summarise(n.pats = n(),
  334. n.death = sum(death.bin),
  335. n.admitted = sum(n_admissions > 0),
  336. mean.admit = mean(n_admissions),
  337. mean.time = mean(total_tih)) %>%
  338. mutate(prop.death = ifelse(n.death >= 10, 100*n.death/n.pats, NA),
  339. prop.admitted = ifelse(n.admitted >= 10, 100*n.admitted/n.pats, NA),
  340. mean.admit = ifelse(n.admitted >= 10, mean.admit, NA),
  341. mean.time = ifelse(n.admitted >= 10, mean.time, NA))
  344. # fit death models
  345. m1 <- glm(death.bin ~ ED + diagage + diagyear + imd + comorb_score + datasource,
  346. data = no_death,
  347. family = binomial(link = "logit"))
  349. death.int <- exp(confint(m1, "EDTRUE"))
  351. m2 <- glm(death.bin ~ ED,
  352. data = no_death,
  353. family = binomial(link = "logit"))
  355. death.crude.int <- exp(confint(m2, "EDTRUE"))
  357. # fit hospitalisation models
  358. if (offset) {
  359. m3 <- glmmTMB(total_tih ~ ED + diagage + diagyear + imd + comorb_score + datasource + offset(log(death_offset)),
  360. data = no_death,
  361. family = nbinom2)
  363. m4 <- glmmTMB(total_tih ~ ED + offset(log(death_offset)),
  364. data = no_death,
  365. family = nbinom2)
  366. } else {
  367. m3 <- glmmTMB(total_tih ~ ED + diagage + diagyear + imd + comorb_score + datasource,
  368. data = no_death,
  369. family = nbinom2)
  371. m4 <- glmmTMB(total_tih ~ ED,
  372. data = no_death,
  373. family = nbinom2)
  374. }
  376. time.int <- exp(confint(m3, "EDTRUE"))
  378. time.crude.int <- exp(confint(m4, "EDTRUE"))
  380. if (!(offset | sensitivity_analysis | exclude_covid)) {
  381. main_prog <- tibble(n.ED = obs$n.pats[obs$ED], n.EDdeath = obs$n.death[obs$ED], death.obs_true = obs$prop.death[obs$ED], n.NED = obs$n.pats[!obs$ED], n.NEDdeath = obs$n.death[!obs$ED], death.obs_false = obs$prop.death[!obs$ED], death.crude = exp(coef(m2)[2]), death.lowcrude = death.crude.int[1], death.upcrude = death.crude.int[2], death = exp(coef(m1)[2]), death.low = death.int[1], death.up = death.int[2], excess.mort = excess_mort_res$t0, excess.lowmort = excess_mort_ci$normal[2], excess.upmort = excess_mort_ci$normal[3], n.EDadmitted = obs$n.admitted[obs$ED], perc.EDadmitted = obs$prop.admitted[obs$ED], time.obs_true = obs$mean.time[obs$ED], n.NEDadmitted = obs$n.admitted[!obs$ED], perc.NEDadmitted = obs$prop.admitted[!obs$ED], time.obs_false = obs$mean.time[!obs$ED], time.crude = time.crude.int[3], time.lowcrude = time.crude.int[1], time.upcrude = time.crude.int[2], time = time.int[3], time.low = time.int[1], time.up = time.int[2], excess.tih = excess_tih_res$t0, excess.lowtih = excess_tih_ci$normal[2], excess.uptih = excess_tih_ci$normal[3])
  382. } else {
  383. main_prog <- tibble(n.ED = obs$n.pats[obs$ED], n.EDdeath = obs$n.death[obs$ED], death.obs_true = obs$prop.death[obs$ED], n.NED = obs$n.pats[!obs$ED], n.NEDdeath = obs$n.death[!obs$ED], death.obs_false = obs$prop.death[!obs$ED], death.crude = exp(coef(m2)[2]), death.lowcrude = death.crude.int[1], death.upcrude = death.crude.int[2], death = exp(coef(m1)[2]), death.low = death.int[1], death.up = death.int[2], n.EDadmitted = obs$n.admitted[obs$ED], perc.EDadmitted = obs$prop.admitted[obs$ED], time.obs_true = obs$mean.time[obs$ED], n.NEDadmitted = obs$n.admitted[!obs$ED], perc.NEDadmitted = obs$prop.admitted[!obs$ED], time.obs_false = obs$mean.time[!obs$ED], time.crude = time.crude.int[3], time.lowcrude = time.crude.int[1], time.upcrude = time.crude.int[2], time = time.int[3], time.low = time.int[1], time.up = time.int[2])
  384. }
  386. return(list("main_prog" = main_prog, "diagyr_prog" = obs_diagyr))
  387. }
  389. assess_gender_prognosis <- function(patients_admit) {
  390. # compare gender association with death
  391. # restrict to patients who didn't die on diagnosis date
  392. no_death <- patients_admit %>%
  393. filter((diagdate != deathdate) | is.na(deathdate),
  394. !is.na(imd)) %>%
  395. mutate(datasource = as.factor(datasource),
  396. diagyear = diagyear - 1999)
  398. if (sum(!is.na(no_death$death) & (no_death$death < 0))) {
  399. errorCondition('Patient has deathdate before emergency diagnosis')
  400. }
  402. no_death <- no_death %>%
  403. mutate(death.bin = (!is.na(death)) & (death > 0) & (death <= 365),
  404. death_offset = ifelse(death.bin, death, 365))
  407. m1 <- glm(death.bin ~ ED + gender*(diagage + diagyear + imd + comorb_score + datasource),
  408. data = no_death,
  409. family = binomial(link = "logit"))
  411. m2 <- glm(death.bin ~ gender*(ED + diagage + diagyear + imd + comorb_score + datasource),
  412. data = no_death,
  413. family = binomial(link = "logit"))
  415. death.test <- lrtest(m1, m2)
  418. if (offset) {
  419. m3 <- glmmTMB(total_tih ~ ED + gender*(diagage + diagyear + imd + comorb_score + datasource) + offset(log(death_offset)),
  420. data = no_death,
  421. family = nbinom2)
  423. m4 <- glmmTMB(total_tih ~ gender*(ED + diagage + diagyear + imd + comorb_score + datasource) + offset(log(death_offset)),
  424. data = no_death,
  425. family = nbinom2)
  426. } else {
  427. m3 <- glmmTMB(total_tih ~ ED + gender*(diagage + diagyear + imd + comorb_score + datasource),
  428. data = no_death,
  429. family = nbinom2)
  431. m4 <- glmmTMB(total_tih ~ gender*(ED + diagage + diagyear + imd + comorb_score + datasource),
  432. data = no_death,
  433. family = nbinom2)
  434. }
  435. hosp.test <- lrtest(m3, m4)
  437. return(tibble(death.p = death.test$`Pr(>Chisq)`[2], hosp.p = hosp.test$`Pr(>Chisq)`[2]))
  439. }
  441. # 2. Loop to process data ----
  442. res <- tibble(gender = c(), pln = c(), condition = c(), ED = c(), n.pats = c(), ED.p = c())
  443. timeres <- tibble(gender = c(), pln = c(), condition = c(), diagyear = c(), prop = c(), low.ci = c(), up.ci = c())
  444. ageres <- tibble(gender = c(), pln = c(), condition = c(), diagage = c(), prop = c(), low.ci = c(), up.ci = c())
  445. imdres <- tibble(gender = c(), pln = c(), condition = c(), imd = c(), prop = c(), low.ci = c(), up.ci = c())
  446. prognosis <- tibble(gender = c(), pln = c(), condition = c(), n.ED = c(), n.EDdeath = c(), death.obs_true = c(), n.NED = c(), n.NEDdeath = c(), death.obs_false = c(), death.crude = c(), death.lowcrude = c(), death.upcrude = c(), death = c(), death.low = c(), death.up = c(), excess.mort = c(), excess.lowmort = c(), excess.upmort = c(), n.EDadmitted = c(), perc.EDadmitted = c(), time.obs_true = c(), n.NEDadmitted = c(), perc.NEDadmitted = c(), time.obs_false = c(), time.crude = c(), time.lowcrude = c(), time.upcrude = c(), time = c(), time.low = c(), time.up = c(), excess.tih = c(), excess.lowtih = c(), excess.uptih = c())
  447. diagyr_prognosis <- tibble(gender = c(), pln = c(), condition = c(), diagyear = c(), ED = c(), n.pats = c(), n.death = c(), n.admitted = c(), prop.death = c(), prop.admitted = c(), mean.admit = c(), mean.time = c())
  448. gender_diffs <- tibble(pln = c(), condition = c(), death.p = c(), hosp.p = c())
  450. tidy_res <- function(patients_admit, group_var, cond1, pln1) {
  451. patients_admit %>%
  452. group_by({{group_var}}, gender) %>%
  453. summarise(prop = 100*mean(ED, na.rm = T),
  454. low.ci = 100*prop.test(sum(ED), n())$conf.int[1],
  455. up.ci = 100*prop.test(sum(ED), n())$conf.int[2],
  456. n.ED = sum(ED),
  457. n.pats = n()) %>%
  458. mutate(condition = cond1,
  459. pln = pln1)
  461. }
  463. for (cond in conditions) {
  464. cat("# ", cond, "\n")
  465. for (plnx in c('gold', 'aurum')) {
  466. cat("## ", plnx, "\n")
  467. patients_admit <- extract_EDs(plnx, cond)
  468. patients_admit <- append_hospitalisations(patients_admit, plnx, cond)
  470. # ED frequency and trends
  471. if (!(cond %in% c('ovarian_cancer', 'PCOS'))) {
  472. patients_admit <- patients_admit %>%
  473. filter(gender %in% c('Men', 'Women'))
  475. # compare ED frequency
  476. EDcross <- table(patients_admit$ED, patients_admit$gender)
  477. EDp <- chisq.test(EDcross)
  479. if (any(EDp$expected < 5)) {
  480. cat("Warning - ", cond, "EP chi-sq has values less than 5")
  481. knitr::kable(EDp$expected)
  482. }
  484. temp_res <- patients_admit %>%
  485. group_by(gender) %>%
  486. summarise(ED = sum(ED),
  487. n.pats = n_distinct(patid)) %>%
  488. mutate(condition = cond,
  489. pln = plnx,
  490. ED.p = EDp$p.value)
  492. gender_diffs <- gender_diffs %>%
  493. rbind(assess_gender_prognosis(patients_admit) %>%
  494. mutate(condition = cond,
  495. pln = plnx))
  496. } else {
  497. temp_res <- patients_admit %>%
  498. group_by(gender) %>%
  499. summarise(ED = sum(ED),
  500. n.pats = n_distinct(patid)) %>%
  501. mutate(condition = cond,
  502. pln = plnx,
  503. ED.p = NA)
  504. }
  506. res <- res %>%
  507. rbind(temp_res)
  509. timeres <- timeres %>%
  510. rbind(tidy_res(patients_admit, diagyear, cond, plnx))
  512. ageres <- ageres %>%
  513. rbind(tidy_res(patients_admit %>%
  514. mutate(age_group = case_match(diagage,
  515. seq(0, 15) ~ "< 16",
  516. seq(16, 20) ~ "16-20",
  517. seq(21, 25) ~ "21-25",
  518. seq(26, 30) ~ "26-30",
  519. seq(31, 35) ~ "31-35",
  520. seq(36, 40) ~ "36-40",
  521. seq(41, 45) ~ "41-45",
  522. seq(46, 50) ~ "46-50",
  523. seq(51, 55) ~ "51-55",
  524. seq(56, 60) ~ "56-60",
  525. seq(61, 65) ~ "61-65",
  526. seq(66, 70) ~ "66-70",
  527. seq(71, 75) ~ "71-75",
  528. seq(76, 80) ~ "76-80",
  529. seq(81, 90) ~ "81-90",
  530. .default = "> 90")),
  531. age_group, cond, plnx))
  533. imdres <- imdres %>%
  534. rbind(tidy_res(patients_admit, imd, cond, plnx))
  536. for (gen in c('Men', 'Women')) {
  537. if (cond %in% c('ovarian_cancer', 'PCOS') & (gen == "Men")) next
  539. # patient characteristics
  540. cat("### ", gen, "\n")
  542. t <- patients_admit %>%
  543. filter(gender == gen) %>%
  544. mutate(imd5 = case_when(imd %in% seq(1, 4) ~ '1 - Least deprived',
  545. imd %in% seq(5, 8) ~ '2',
  546. imd %in% seq(9, 12) ~ '3',
  547. imd %in% seq(13, 16) ~ '4',
  548. imd %in% seq(17, 20) ~ '5 - Most deprived',
  549. T ~ as.character(imd))) %>%
  550. select(ED, diagage, comorb_score, imd5, datasource) %>%
  551. tbl_summary(by = ED,
  552. type = all_continuous() ~ "continuous2",
  553. statistic = list(
  554. all_continuous() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})"),
  555. all_categorical() ~ "{n} ({p}%)"
  556. ),
  557. digits = list(all_continuous() ~ 1,
  558. all_categorical() ~ 1),
  559. label = list(diagage = "Age at diagnosis", comorb_score = "Comorbidity score", imd5 = "IMD quintile", datasource = "Source of diagnosis")) %>%
  560. add_n()
  562. cat("\n")
  563. cat(knitr::knit_print(t))
  565. # prognosis
  566. tmp_prog <- assess_prognosis(patients_admit %>%
  567. filter(gender == gen),
  568. plnx, cond)
  569. prognosis <- prognosis %>%
  570. rbind(tmp_prog$main_prog %>%
  571. mutate(gender = gen,
  572. condition = cond,
  573. pln = plnx))
  575. diagyr_prognosis <- diagyr_prognosis %>%
  576. rbind(tmp_prog$diagyr_prog %>%
  577. mutate(gender = gen,
  578. condition = cond,
  579. pln = plnx))
  580. }
  581. }
  582. }
  584. # clean up tables
  585. res <- res %>%
  586. rowwise() %>%
  587. mutate(prop = 100*prop.test(ED, n.pats)$estimate,
  588. low.ci = 100*prop.test(ED, n.pats)$conf.int[1],
  589. up.ci = 100*prop.test(ED, n.pats)$conf.int[2]) %>%
  590. mutate(s = glue("{signif(prop, 3)}% ({signif(low.ci, 3)}%, {signif(up.ci, 3)}%) {ED.p}")) %>%
  591. mutate(condition = case_match(condition,
  592. "AS" ~ "Axial spondyloarthritis",
  593. "brain_tumours" ~ "Brain tumours",
  594. "CHD" ~ "CHD",
  595. "COPD" ~ "COPD",
  596. "IBD" ~ "IBD",
  597. "MS" ~ "Multiple sclerosis",
  598. "PCOS" ~ "PCOS",
  599. "RA" ~ "Rheumatoid arthritis",
  600. "SBE" ~ "SBE",
  601. "TB" ~ "Tuberculosis",
  602. "celiac" ~ "Coeliac disease",
  603. "colon_cancer" ~ "Colon cancer",
  604. "lung_cancer" ~ "Lung cancer",
  605. "lyme" ~ "Lyme disease",
  606. "ovarian_cancer" ~ "Ovarian cancer",
  607. "pancreatic_cancer" ~ "Pancreatic cancer",
  608. "parkinsons" ~ "Parkinson's disease",
  609. "schizophrenia" ~ "Schizophrenia"))
  611. timeres <- timeres %>%
  612. mutate(grp = case_match(condition,
  613. c("AS", "brain_tumours", "celiac", "CHD", "colon_cancer", "COPD", "IBD", "lung_cancer", "lyme") ~ "1",
  614. .default = "2"),
  615. condition = case_match(condition,
  616. "AS" ~ "Axial spondyloarthritis",
  617. "brain_tumours" ~ "Brain tumours",
  618. "CHD" ~ "CHD",
  619. "COPD" ~ "COPD",
  620. "IBD" ~ "IBD",
  621. "MS" ~ "Multiple sclerosis",
  622. "PCOS" ~ "PCOS",
  623. "RA" ~ "Rheumatoid arthritis",
  624. "SBE" ~ "SBE",
  625. "TB" ~ "Tuberculosis",
  626. "celiac" ~ "Coeliac disease",
  627. "colon_cancer" ~ "Colon cancer",
  628. "lung_cancer" ~ "Lung cancer",
  629. "lyme" ~ "Lyme disease",
  630. "ovarian_cancer" ~ "Ovarian cancer",
  631. "pancreatic_cancer" ~ "Pancreatic cancer",
  632. "parkinsons" ~ "Parkinson's disease",
  633. "schizophrenia" ~ "Schizophrenia"))
  635. ageres <- ageres %>%
  636. mutate(age_group = factor(age_group, levels = c("< 16", "16-20", "21-25", "26-30", "31-35", "36-40", "41-45", "46-50", "51-55", "56-60", "61-65", "66-70", "71-75", "76-80", "81-90", "> 90"), ordered = T)) %>%
  637. mutate(age_mid = case_match(age_group,
  638. "< 16" ~ 8,
  639. "16-20" ~ 18,
  640. "21-25" ~ 23,
  641. "26-30" ~ 28,
  642. "31-35" ~ 33,
  643. "36-40" ~ 38,
  644. "41-45" ~ 43,
  645. "46-50" ~ 48,
  646. "51-55" ~ 53,
  647. "56-60" ~ 58,
  648. "61-65" ~ 63,
  649. "66-70" ~ 68,
  650. "71-75" ~ 73,
  651. "76-80" ~ 78,
  652. "81-90" ~ 85,
  653. "> 90" ~ 95),
  654. grp = case_match(condition,
  655. c("AS", "brain_tumours", "celiac", "CHD", "colon_cancer", "COPD", "IBD", "lung_cancer", "lyme") ~ "1",
  656. .default = "2"),
  657. condition = case_match(condition,
  658. "AS" ~ "Axial spondyloarthritis",
  659. "brain_tumours" ~ "Brain tumours",
  660. "CHD" ~ "CHD",
  661. "COPD" ~ "COPD",
  662. "IBD" ~ "IBD",
  663. "MS" ~ "Multiple sclerosis",
  664. "PCOS" ~ "PCOS",
  665. "RA" ~ "Rheumatoid arthritis",
  666. "SBE" ~ "SBE",
  667. "TB" ~ "Tuberculosis",
  668. "celiac" ~ "Coeliac disease",
  669. "colon_cancer" ~ "Colon cancer",
  670. "lung_cancer" ~ "Lung cancer",
  671. "lyme" ~ "Lyme disease",
  672. "ovarian_cancer" ~ "Ovarian cancer",
  673. "pancreatic_cancer" ~ "Pancreatic cancer",
  674. "parkinsons" ~ "Parkinson's disease",
  675. "schizophrenia" ~ "Schizophrenia"))
  677. imdres <- imdres %>%
  678. mutate(grp = case_match(condition,
  679. c("AS", "brain_tumours", "celiac", "CHD", "colon_cancer", "COPD", "IBD", "lung_cancer", "lyme") ~ "1",
  680. .default = "2"),
  681. condition = case_match(condition,
  682. "AS" ~ "Axial spondyloarthritis",
  683. "brain_tumours" ~ "Brain tumours",
  684. "CHD" ~ "CHD",
  685. "COPD" ~ "COPD",
  686. "IBD" ~ "IBD",
  687. "MS" ~ "Multiple sclerosis",
  688. "PCOS" ~ "PCOS",
  689. "RA" ~ "Rheumatoid arthritis",
  690. "SBE" ~ "SBE",
  691. "TB" ~ "Tuberculosis",
  692. "celiac" ~ "Coeliac disease",
  693. "colon_cancer" ~ "Colon cancer",
  694. "lung_cancer" ~ "Lung cancer",
  695. "lyme" ~ "Lyme disease",
  696. "ovarian_cancer" ~ "Ovarian cancer",
  697. "pancreatic_cancer" ~ "Pancreatic cancer",
  698. "parkinsons" ~ "Parkinson's disease",
  699. "schizophrenia" ~ "Schizophrenia"))
  701. prognosis <- prognosis %>%
  702. left_join(gender_diffs, by = join_by(condition, pln)) %>%
  703. mutate(condition = case_match(condition,
  704. "AS" ~ "Axial spondyloarthritis",
  705. "brain_tumours" ~ "Brain tumours",
  706. "CHD" ~ "CHD",
  707. "COPD" ~ "COPD",
  708. "IBD" ~ "IBD",
  709. "MS" ~ "Multiple sclerosis",
  710. "PCOS" ~ "PCOS",
  711. "RA" ~ "Rheumatoid arthritis",
  712. "SBE" ~ "SBE",
  713. "TB" ~ "Tuberculosis",
  714. "celiac" ~ "Coeliac disease",
  715. "colon_cancer" ~ "Colon cancer",
  716. "lung_cancer" ~ "Lung cancer",
  717. "lyme" ~ "Lyme disease",
  718. "ovarian_cancer" ~ "Ovarian cancer",
  719. "pancreatic_cancer" ~ "Pancreatic cancer",
  720. "parkinsons" ~ "Parkinson's disease",
  721. "schizophrenia" ~ "Schizophrenia"))
  723. diagyr_prognosis <- diagyr_prognosis %>%
  724. mutate(grp = case_match(condition,
  725. c("AS", "brain_tumours", "celiac", "CHD", "colon_cancer", "COPD", "IBD", "lung_cancer", "lyme") ~ "1",
  726. .default = "2"),
  727. condition = case_match(condition,
  728. "AS" ~ "Axial spondyloarthritis",
  729. "brain_tumours" ~ "Brain tumours",
  730. "CHD" ~ "CHD",
  731. "COPD" ~ "COPD",
  732. "IBD" ~ "IBD",
  733. "MS" ~ "Multiple sclerosis",
  734. "PCOS" ~ "PCOS",
  735. "RA" ~ "Rheumatoid arthritis",
  736. "SBE" ~ "SBE",
  737. "TB" ~ "Tuberculosis",
  738. "celiac" ~ "Coeliac disease",
  739. "colon_cancer" ~ "Colon cancer",
  740. "lung_cancer" ~ "Lung cancer",
  741. "lyme" ~ "Lyme disease",
  742. "ovarian_cancer" ~ "Ovarian cancer",
  743. "pancreatic_cancer" ~ "Pancreatic cancer",
  744. "parkinsons" ~ "Parkinson's disease",
  745. "schizophrenia" ~ "Schizophrenia"))
  747. # save all analysis results as csvs
  748. if (!offset) {
  749. write_csv(res, glue('{results_filepath}/res.csv'))
  750. write_csv(timeres, glue('{results_filepath}/timeres.csv'))
  751. write_csv(ageres, glue('{results_filepath}/ageres.csv'))
  752. write_csv(imdres, glue('{results_filepath}/imdres.csv'))
  753. write_csv(diagyr_prognosis, glue('{results_filepath}/diagyear_prognosis.csv'))
  754. }
  755. write_csv(prognosis, glue('{results_filepath}/prognosis.csv'))
  757. cat(glue('\nAll results saved at {results_filepath}'))
  758. cat('\nPlease update analysis_dt in A0_global_vars_R.R accordingly \n')
  759. ```
  762. ```{r session_info, echo=FALSE, message = FALSE, results = "asis"}
  763. cat('# Session information\n')
  764. print(sessionInfo())
  765. ```

C2_run_analyses.Rmd at commit a082a9d, under MIT · at the source

Overview

Authors: Emma Whitfield1, Becky White1, Matthew E Barclay1, Meena Rafiq1,2, Nadine Zakkak1,3, Marta Berglund1, Hardeep Singh4, Sean McPhail5, Spiros Denaxas6,7,8,9,10, Georgios Lyratzopoulos1
ORCID iDs: Emma Whitfield, Becky White, Matthew E Barclay, Meena Rafiq, Nadine Zakkak, Marta Berglund, Spiros Denaxas, Georgios Lyratzopoulos
  1. ECHO (Epidemiology of Cancer Healthcare & Outcomes), Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, UCL (University College London), London, United Kingdom
  2. Department of General Practice and Primary Care, University of Melbourne, Melbourne, Australia
  3. Cancer Intelligence, Cancer Research UK, London, United Kingdom
  4. Safety, Quality & Well-Being Institute, Houston Methodist, Houston, Texas, United States of America
  5. National Disease Registration Service, NHS England, Leeds, United Kingdom
  6. Institute of Health Informatics, UCL, London, United Kingdom
  7. Interdisciplinary Transformation University, Linz, Austria
  8. Health Data Research UK, London, United Kingdom
  9. UCL Hospitals Biomedical Research Centre, London, United Kingdom
  10. British Heart Foundation Data Science Centre, London, United Kingdom
Institutions: University College London (United Kingdom); The University of Melbourne (Australia); Cancer Research UK (United Kingdom); Houston Methodist (United States); NHS England; British Heart Foundation (United Kingdom); UCL Biomedical Research Centre (United Kingdom); Health Data Research UK (United Kingdom); Interdisciplinary Transformation University Austria (Austria)
Journal: PLoS medicine, volume 23, issue 8, article e1005182
Dates: received 5 January 2026; accepted 3 July 2026; published online 25 August 2026
Type: Research article · Language: English
License: none stated
Identifiers: DOI 10.1371/journal.pmed.1005182 · PMID 42640879 · PMCID PMC13505939 · OpenAlex W7204193769
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: other (modality), human (organism), clinical / translational (subfield)
Methods: Machine learning, Statistics
MeSH: Electronic Health Records*, Emergency Service, Hospital*, Noncommunicable Diseases*, Adolescent, Adult, Aged, Aged, 80 and over, Child, Cohort Studies, Emergency Room Visits, England, Female, Humans, Male, Middle Aged, Prognosis, Young Adult (* major topic)
Topic: Hematological disorders and diagnostics (Emergency Medicine, Medicine), according to OpenAlex
Funding: Wellcome Trust (218529/Z/19/Z)
Citations: not cited yet (Europe PMC); 61 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (none stated) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repositories

Its files are read in the Code ↔ Paper reader above, with 7 matches between paragraphs and lines of code.

Zenodo 13710755

License: CC-BY-4.0
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Study population”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
2 files
At the source: doi.org/10.5281/zenodo.13710755

Zenodo 16840511

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the text, “Statistical analyses”
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), ggplot2 (1 file), glmmTMB (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
  • 27 September 2026: the link answers (HTTP 200)
9 files
At the source: doi.org/10.5281/zenodo.16840511

ekw26/atlas-phenotypes

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 92d97fb92c2624c5ec03159c2c94c17cb2b70e4b, 6 September 2024
Size: 38 files, 0 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
2 files
At the source: github.com/ekw26/Atlas-phenotypes/tree/v1.0

ekw26/atlas-emergencies

License: MIT
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: a082a9d528f098780a249bcd82d04a64d1439973, 24 June 2026
Languages: R (4), Python (3)
Size: 9 files, 7 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, 3 notebooks
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: tidyverse (3 files), ggplot2 (1 file), glmmTMB (1 file), patchwork (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
9 files
At the source: github.com/ekw26/Atlas-emergencies/tree/v1.3.1

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 4 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 14 scripts, each with its path and the digest of its content;
  • 7 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The paper has a data availability statement. Its license (none stated) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • no repository, dataset or request procedure was recognized in it

Read it in the paper: doi.org/10.1371/journal.pmed.1005182.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 17 MeSH terms, 1 funder, 50 references.

Cite

This paper

Whitfield, E., White, B., Barclay, M. E., Rafiq, M., Zakkak, N., Berglund, M., Singh, H., McPhail, S., Denaxas, S., & Lyratzopoulos, G. (2026). Frequency and prognostic outcomes of emergency diagnosis in 13 non-neoplastic conditions in England: A population-based cohort study using linked electronic health records of 1.7 million patients. PLoS medicine, 23(8), e1005182. https://doi.org/10.1371/journal.pmed.1005182

BibTeX

@article{whitfield2026frequency,
author = {Whitfield, Emma and White, Becky and Barclay, Matthew E and Rafiq, Meena and Zakkak, Nadine and Berglund, Marta and Singh, Hardeep and McPhail, Sean and Denaxas, Spiros and Lyratzopoulos, Georgios},
title = {{Frequency and prognostic outcomes of emergency diagnosis in 13 non-neoplastic conditions in England: A population-based cohort study using linked electronic health records of 1.7 million patients}},
journal = {PLoS medicine},
year = {2026},
month = aug,
volume = {23},
number = {8},
pages = {e1005182},
publisher = {PLOS},
issn = {1549-1277},
doi = {10.1371/journal.pmed.1005182},
url = {https://doi.org/10.1371/journal.pmed.1005182},
pmid = {42640879},
pmcid = {PMC13505939}
}

RIS

TY - JOUR
AU - Whitfield, Emma
AU - White, Becky
AU - Barclay, Matthew E
AU - Rafiq, Meena
AU - Zakkak, Nadine
AU - Berglund, Marta
AU - Singh, Hardeep
AU - McPhail, Sean
AU - Denaxas, Spiros
AU - Lyratzopoulos, Georgios
TI - Frequency and prognostic outcomes of emergency diagnosis in 13 non-neoplastic conditions in England: A population-based cohort study using linked electronic health records of 1.7 million patients
T2 - PLoS medicine
J2 - PLoS Med
PY - 2026
DA - 2026/08/25
VL - 23
IS - 8
SP - e1005182
SN - 1549-1277
PB - PLOS
DO - 10.1371/journal.pmed.1005182
UR - https://doi.org/10.1371/journal.pmed.1005182
LA - en
ER -

CSL-JSON

{
"id": "10.1371/journal.pmed.1005182",
"type": "article-journal",
"title": "Frequency and prognostic outcomes of emergency diagnosis in 13 non-neoplastic conditions in England: A population-based cohort study using linked electronic health records of 1.7 million patients",
"container-title": "PLoS medicine",
"author": [
{
"family": "Whitfield",
"given": "Emma"
},
{
"family": "White",
"given": "Becky"
},
{
"family": "Barclay",
"given": "Matthew E"
},
{
"family": "Rafiq",
"given": "Meena"
},
{
"family": "Zakkak",
"given": "Nadine"
},
{
"family": "Berglund",
"given": "Marta"
},
{
"family": "Singh",
"given": "Hardeep"
},
{
"family": "McPhail",
"given": "Sean"
},
{
"family": "Denaxas",
"given": "Spiros"
},
{
"family": "Lyratzopoulos",
"given": "Georgios"
}
],
"container-title-short": "PLoS Med",
"volume": "23",
"issue": "8",
"page": "e1005182",
"DOI": "10.1371/journal.pmed.1005182",
"PMID": "42640879",
"PMCID": "PMC13505939",
"ISSN": "1549-1277",
"publisher": "PLOS",
"URL": "https://doi.org/10.1371/journal.pmed.1005182",
"language": "en",
"issued": {
"date-parts": [
[
2026,
8,
25
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.1242/jeb.252086 [code]
Transcriptional predictors of rescue behaviour in ants.
Journal: The Journal of experimental biology
In common: glmmTMB, patchwork, ggplot2, 1 other tool, 1 reference
[2] doi:10.1038/s41514-026-00391-9 [code]
Region-specific transcriptional signatures of brain aging in the absence of neuropathology at the single-cell level.
Journal: npj aging
In common: glmmTMB, patchwork, ggplot2, 1 other tool, 1 reference
[3] doi:10.3389/fnsyn.2026.1832103 [code]
Automated ROI detection allows rapid quantification of synaptic activity across tens of thousands of synapses in cell culture.
Journal: Frontiers in synaptic neuroscience
In common: glmmTMB, ggplot2, tidyverse, 1 reference
[4] doi:10.1038/s41467-026-76675-1 [code]
Long-read proteogenomic atlas of human neuronal differentiation reveals isoform diversity informing neurodevelopmental risk mechanisms.
Journal: Nature communications
In common: glmmTMB, patchwork, ggplot2, 1 other tool
[5] doi:10.1093/braincomms/fcag217 [code]
Investigation of stress hormones across multiday seizure cycles.
Journal: Brain communications
In common: glmmTMB, patchwork, ggplot2, 1 other tool
[6] doi:10.1038/s41586-026-10444-4 [code]
A brain reward circuit inhibited by next-generation weight-loss drugs in mice.
Journal: Nature
In common: glmmTMB, patchwork, ggplot2, 1 other tool
[7] doi:10.3390/ijms27135713 [code]
Chronic Administration of Marinobufagenin in Mice Causes Hyperlocomotion and Decrease in Anxiety by Altering Monoamine Turnover Unaccompanied by Motor Deficits or Oxidative Stress.
Journal: International journal of molecular sciences
In common: glmmTMB, patchwork, ggplot2, 1 other tool
[8] doi:10.1016/j.celrep.2026.117505 [code]
Impaired spatial coding and neuronal hyperactivity in the medial entorhinal cortex of aged APP knock-in mice.
Journal: Cell reports
In common: glmmTMB, patchwork, ggplot2, 1 other tool
[9] doi:10.1038/s41467-026-76676-0 [code]
Determinants of functional burden pleiotropy and gene dosage responses across human traits.
Journal: Nature communications
In common: patchwork, ggplot2, tidyverse, 1 reference
[10] doi:10.1038/s41514-026-00439-w [code]
Effects of a three-month exercise programme on cognition, mood and neurogenesis: the NeuroFit randomised controlled trial.
Journal: npj aging
In common: glmmTMB, ggplot2, tidyverse

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.