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Reading Disabilities With and Without Co-Occurring ADHD: No Evidence for Additive Impact on Reading Skills.

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The 2 matches
  1. [1] § Study 2 Method › Clinical grouping ↔ Marks2026_SSR_RD+ADHD.R, lines 1–56 · score 0.68 · Borderline Intellectual Functioning, Intellectual Disability, co occurring, autism, epilepsy, language
  2. [2] § Study 2 results › Reading in ADHD alone ↔ Marks2026_SSR_RD+ADHD.R, lines 58–131 · score 0.60 · ADHD Hyperactive, ADHD Inattentive, ADHD Combined, Impulsive

Paper

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The authors' code

R · 330 lines · 13 KB · no license · 2 matches

  1. # Analytic code for:
  2. ### Reading Disabilities With and Without Co-Occurring ADHD:
  3. ### No Evidence for Additive Impact on Reading Skills (Marks et al., 2026)
  4. library(dplyr)
  5. library(tibble)
  6. library(tidyverse)
  7. library(psych)
  8. library(rstatix)
  9. library(ggpubr)
  10. library(tableone)
  11. library(purrr)
  12. library(effsize)
  14. ### Import data
  15. HBNall <- read.csv("~/Library/CloudStorage/Box-Box/2_Manuscripts/In Progress/2025 READ RD+ADHD/HBNdata-2025-02-07T19_56_45.csv")
  16. READdata <- read.csv("~/Library/CloudStorage/Box-Box/2_Manuscripts/In Progress/2025 READ RD+ADHD/2025-07-10_DYS+ADHD_N163.csv")
  18. ### Clean and organize HBN data
  19. ### Filters
  20. HBN <- HBNall %>%
  21. filter(Basic_Demos.Age > 7) %>%
  22. filter(Basic_Demos.Age < 13)
  24. HBNflags <- HBN %>% mutate(autism_flag = ifelse(
  25. reduce(across(everything(), ~ str_detect(.x, regex("autism|asd", ignore_case = TRUE))), `|`),
  26. "Autism", NA)) %>%
  27. mutate(adhd_flag = ifelse(
  28. reduce(across(everything(), ~ str_detect(.x, regex("adhd|attention", ignore_case = TRUE))), `|`),
  29. "ADHD", NA)) %>%
  30. mutate(dys_flag = ifelse(
  31. reduce(across(everything(), ~ str_detect(.x, regex("dyslexia|reading", ignore_case = TRUE))), `|`),
  32. "Dyslexia", NA)) %>%
  33. mutate(math_flag = ifelse(
  34. reduce(across(everything(), ~ str_detect(.x, regex("math", ignore_case = TRUE))), `|`),
  35. "Dyscalculia", NA)) %>%
  36. mutate(dld_flag = ifelse(
  37. reduce(across(everything(), ~ str_detect(.x, regex("language", ignore_case = TRUE))), `|`),
  38. "Lang-Comm", NA)) %>%
  39. mutate(anx_flag = ifelse(
  40. reduce(across(everything(), ~ str_detect(.x, regex("anxiety", ignore_case = TRUE))), `|`),
  41. "Anxiety", NA)) %>%
  42. mutate(dep_flag = ifelse(
  43. reduce(across(everything(), ~ str_detect(.x, regex("depression", ignore_case = TRUE))), `|`),
  44. "Depression", NA)) %>%
  45. mutate(exclude_flag = ifelse(
  46. reduce(across(everything(), ~ str_detect(.x, regex("Borderline Intellectual Functioning|Intellectual Disability|Epilepsy|Incomplete Eval", ignore_case = TRUE))), `|`),
  47. "exclude", NA))
  49. HBNflags[HBNflags == ""] <- NA
  50. HBNflags[HBNflags == "."] <- NA
  52. HBNflags <- HBNflags %>% mutate(nowordreading = ifelse(
  53. is.na(TOWRE.TOWRE_PDE_Scaled) & is.na(TOWRE.TOWRE_SWE_Scaled) & is.na(WIAT.WIAT_Pseudo_Stnd) & is.na(WIAT.WIAT_Word_Stnd),
  54. "no word reading", FALSE))
  56. eligibledf <- HBNflags %>% filter(is.na(autism_flag) & is.na(exclude_flag) & nowordreading == 'FALSE')
  58. eligibledf <- eligibledf %>%
  59. mutate(group = case_when(
  60. dys_flag == "Dyslexia" & is.na(adhd_flag) ~ "dys",
  61. is.na(dys_flag) & adhd_flag == "ADHD" ~ "adhd",
  62. dys_flag == "Dyslexia" & adhd_flag == "ADHD" ~ "dys+adhd",
  63. is.na(dys_flag) & is.na(adhd_flag) ~ "neither")
  64. )
  66. eligibledf <- eligibledf %>%
  67. mutate(dys = ifelse(is.na(dys_flag), 0, 1),
  68. math = ifelse(is.na(math_flag), 0, 1),
  69. adhd = ifelse(is.na(adhd_flag), 0, 1),
  70. dld = ifelse(is.na(dld_flag), 0, 1),
  71. depression = ifelse(is.na(dep_flag), 0, 1),
  72. anxiety = ifelse(is.na(anx_flag), 0, 1)
  73. )
  75. eligibledf <- eligibledf %>%
  76. mutate(adhd_type = case_when(
  77. reduce(
  78. across(everything(), ~ str_detect(.x, regex("ADHD-Hyperactive/Impulsive Type", ignore_case = TRUE))),
  79. `|`) ~ "Hyperactive",
  80. reduce(
  81. across(everything(), ~ str_detect(.x, regex("ADHD-Combined Type", ignore_case = TRUE))),
  82. `|`) ~ "Combined",
  83. reduce(
  84. across(everything(), ~ str_detect(.x, regex("ADHD-Inattentive Type", ignore_case = TRUE))),
  85. `|`) ~ "Inattentive", TRUE ~ NA_character_)
  86. )
  88. eligibledf <- eligibledf %>%
  89. select(Identifiers, Barratt.Barratt_Total, Barratt.Barratt_Total_Edu, Barratt.Barratt_Total_Occ, Basic_Demos.Age, Basic_Demos.Sex, Basic_Demos.Study_Site,
  90. PreInt_Demos_Fam.Child_Ethnicity, PreInt_Demos_Fam.Child_Race, PreInt_EduHx.IEP, PreInt_EduHx.repeated_grades, PreInt_TxHx.Past_DX,
  91. C3SR.C3SR_HY_T, C3SR.C3SR_IN_T, SWAN.SWAN_HY, SWAN.SWAN_IN,
  92. CTOPP.CTOPP_EL_S, TOWRE.TOWRE_PDE_Scaled, TOWRE.TOWRE_SWE_Scaled, WIAT.WIAT_Pseudo_Stnd, WIAT.WIAT_Word_Stnd, WIAT.WIAT_RC_Stnd,
  93. WISC.WISC_MR_Scaled, WISC.WISC_FSIQ, WISC.WISC_Vocab_Scaled, DigitSpan.DS_total_scaled,
  94. WISC.WISC_Coding_Scaled, WISC.WISC_SS_Scaled, WISC.WISC_DS_Scaled,
  95. Diagnosis_ClinicianConsensus.DX_01, Diagnosis_ClinicianConsensus.DX_02, Diagnosis_ClinicianConsensus.DX_03, Diagnosis_ClinicianConsensus.DX_04, Diagnosis_ClinicianConsensus.DX_05,
  96. Diagnosis_ClinicianConsensus.DX_06, Diagnosis_ClinicianConsensus.DX_07, Diagnosis_ClinicianConsensus.DX_08, Diagnosis_ClinicianConsensus.DX_09, Diagnosis_ClinicianConsensus.DX_10,
  97. dys_flag, adhd_flag, group, math, dld, depression, anxiety, adhd_type)
  99. eligibledf <- eligibledf %>% mutate(
  100. age = as.numeric(Basic_Demos.Age),
  101. sex = as.numeric(Basic_Demos.Sex),
  102. ses_educ = as.numeric(Barratt.Barratt_Total_Edu),
  103. ses_occ = as.numeric(Barratt.Barratt_Total_Occ),
  104. ses_total = as.numeric(Barratt.Barratt_Total),
  105. swan_hy = as.numeric(SWAN.SWAN_HY),
  106. swan_in = as.numeric(SWAN.SWAN_IN),
  107. conners_hyT = as.numeric(C3SR.C3SR_HY_T),
  108. conners_inT = as.numeric(C3SR.C3SR_IN_T),
  109. ctopp = as.numeric(CTOPP.CTOPP_EL_S),
  110. towre_pde = as.numeric(TOWRE.TOWRE_PDE_Scaled),
  111. towre_swe = as.numeric(TOWRE.TOWRE_SWE_Scaled),
  112. wiat_pseudo = as.numeric(WIAT.WIAT_Pseudo_Stnd),
  113. wiat_word = as.numeric(WIAT.WIAT_Word_Stnd),
  114. wiat_rc = as.numeric(WIAT.WIAT_RC_Stnd),
  115. wisc_mat = as.numeric(WISC.WISC_MR_Scaled),
  116. wisc_vocab = as.numeric(WISC.WISC_Vocab_Scaled),
  117. wisc_ds = as.numeric(WISC.WISC_DS_Scaled),
  118. wisc_coding = as.numeric(WISC.WISC_Coding_Scaled),
  119. wisc_ss = as.numeric(WISC.WISC_SS_Scaled))
  121. clean <- eligibledf %>%
  122. select(Identifiers, age, sex, ses_educ, ses_occ, ses_total, Basic_Demos.Study_Site,
  123. PreInt_Demos_Fam.Child_Ethnicity, PreInt_Demos_Fam.Child_Race, PreInt_EduHx.IEP, PreInt_EduHx.repeated_grades, PreInt_TxHx.Past_DX,
  124. conners_inT, conners_hyT, swan_in, swan_hy,
  125. ctopp, towre_pde, towre_swe, wiat_pseudo, wiat_word, wiat_rc,
  126. wisc_mat, wisc_vocab, wisc_ds, wisc_coding, wisc_ss,
  127. Diagnosis_ClinicianConsensus.DX_01, Diagnosis_ClinicianConsensus.DX_02, Diagnosis_ClinicianConsensus.DX_03, Diagnosis_ClinicianConsensus.DX_04, Diagnosis_ClinicianConsensus.DX_05,
  128. Diagnosis_ClinicianConsensus.DX_06, Diagnosis_ClinicianConsensus.DX_07, Diagnosis_ClinicianConsensus.DX_08, Diagnosis_ClinicianConsensus.DX_09, Diagnosis_ClinicianConsensus.DX_10,
  129. dys_flag, adhd_flag, group, math, dld, depression, anxiety, adhd_type) %>%
  130. mutate(group = fct_relevel(group, "neither", "dys", "dys+adhd", "adhd")) %>%
  131. mutate_at(c('ses_occ', 'ses_educ', 'ses_total'), ~na_if(., 0))
  133. ### Study 2, Table 4: Demographics
  134. HBNDemos <- c("age", "sex", "PreInt_Demos_Fam.Child_Race", "PreInt_Demos_Fam.Child_Ethnicity",
  135. "adhd_type", "anxiety", "depression", "dld",
  136. "ses_educ", "ses_occ", "ses_total")
  137. catVars <- c("sex", "PreInt_Demos_Fam.Child_Race", "PreInt_Demos_Fam.Child_Ethnicity",
  138. "adhd_type", "anxiety", "depression", "dld")
  139. HBNDemosTable <- CreateTableOne(vars = HBNDemos, strata = "group" , data = clean, factorVars = catVars)
  140. HBNDemosmat <- print(HBNDemosTable, quote = FALSE, noSpaces = TRUE, printToggle = FALSE)
  141. #0= White/Caucasian
  142. #1= Black/African American
  143. #2= Hispanic
  144. #3= Asian
  145. #4= Indian
  146. #5= Native American Indian
  147. #6= American Indian/Alaskan Native
  148. #7= Native Hawaiian/Other Pacific Islander
  149. #8= Two or more races
  150. #9= Other race
  151. #10= Unknown
  152. #11=Choose not to specify
  154. ### Study 2, Table 5: Cognition, reading, and behavioral regulation between groups
  155. HBNVars <- c("age", "sex", "ses_total",
  156. "wisc_mat", "wisc_vocab", "ctopp",
  157. "towre_pde", "towre_swe",
  158. "wiat_pseudo", "wiat_word", "wiat_rc",
  159. "swan_in", "swan_hy")
  160. HBNTable <- CreateTableOne(vars = HBNVars, strata = "group" , data = eligibledf)
  161. HBNTablemat <- print(HBNTable, quote = FALSE, noSpaces = TRUE, printToggle = FALSE)
  163. ### ANOVAS ###
  164. aov1 <- aov(wisc_mat ~ group, data=clean)
  165. aov2 <- aov(wisc_vocab ~ group, data=clean)
  166. aov3 <- aov(ctopp ~ group, data=clean)
  167. aov4 <- aov(towre_pde ~ group, data=clean)
  168. aov5 <- aov(towre_swe ~ group, data=clean)
  169. aov6 <- aov(wiat_pseudo ~ group, data=clean)
  170. aov7 <- aov(wiat_word ~ group, data=clean)
  171. aov8 <- aov(wiat_rc ~ group, data=clean)
  173. summary(aov1)
  174. TukeyHSD(aov1)
  175. games_howell_test(clean, wisc_mat ~ group, conf.level = 0.95, detailed = FALSE)
  177. ### VISUALIZATION ###
  178. my_comparisons <- list( c("dys", "dys+adhd"), c("dys", "neither"), c("dys", "adhd"),
  179. c("adhd", "neither"), c("dys+adhd", "neither"),
  180. c("adhd", "dys+adhd"))
  181. hbn_pde <- ggviolin(clean, x = "group", y = "towre_pde", fill = "group",
  182. add = "boxplot", add.params = list(fill = "white"),
  183. legend = NULL,
  184. xlab = FALSE, ylab = FALSE,
  185. title = "Decoding Efficiency",
  186. palette = c("grey", "#298CE7", "#8CE1B7", "#F8E463")) +
  187. scale_x_discrete(labels = c("neither" = "SWOD", "dys" = "RD", "dys+adhd" = "RD+ADHD",
  188. "adhd" = "ADHD")) +
  189. stat_compare_means(comparisons = my_comparisons, label = "p.signif") +
  190. theme(legend.position = "none") + ylim(40, 160)
  191. hbn_pde
  193. ### LINEAR MODELS ###
  194. lm_ctopp <- lm(ctopp ~ group + sex + ses_educ + wisc_mat + wisc_vocab, data=clean)
  195. lm_pde <- lm(towre_pde ~ group + sex + ses_educ + wisc_mat + wisc_vocab, data=clean)
  196. lm_swe <- lm(towre_swe ~ group + sex + ses_educ + wisc_mat + wisc_vocab, data=clean)
  197. lm_word <- lm(wiat_word ~ group + sex + ses_educ + wisc_mat + wisc_vocab, data=clean)
  198. lm_pseudo <- lm(wiat_pseudo ~ group + sex + ses_educ + wisc_mat + wisc_vocab, data=clean)
  199. lm_rc <- lm(wiat_rc ~ group + sex + ses_educ + wisc_mat + wisc_vocab, data=clean)
  201. summary(lm_ctopp)
  202. eta_squared(lm_ctopp)
  204. ### SUPPLEMENT: ALL PAIRWISE COMPARISONS ###
  205. HBNVars <- c("age", "sex", "ses_total", "wisc_vocab", "ctopp", "towre_pde", "towre_swe", "wiat_pseudo", "wiat_word", "wiat_rc")
  207. # =========================
  208. # HEDGES' g
  209. # =========================
  210. hedges_g_safe <- function(x, g)
  211. {
  213. g <- droplevels(g)
  214. lv <- levels(g)
  216. if (length(lv) != 2) return(NA_real_)
  218. x1 <- x[g == lv[1]]
  219. x2 <- x[g == lv[2]]
  221. n1 <- sum(!is.na(x1))
  222. n2 <- sum(!is.na(x2))
  224. if (n1 < 2 || n2 < 2) return(NA_real_)
  226. sd1 <- sd(x1, na.rm = TRUE)
  227. sd2 <- sd(x2, na.rm = TRUE)
  229. sp <- sqrt(((n1 - 1) * sd1^2 + (n2 - 1) * sd2^2) /
  230. (n1 + n2 - 2))
  232. if (is.na(sp) || sp == 0) return(NA_real_)
  234. d <- (mean(x1, na.rm = TRUE) -
  235. mean(x2, na.rm = TRUE)) / sp
  237. J <- 1 - (3 / (4 * (n1 + n2) - 9))
  239. d * J}
  241. # =========================
  242. # ANOVA
  243. # =========================
  244. run_anova <- function(var, data) {
  246. d_sub <- data %>% filter(!is.na(.data[[var]]))
  248. if (n_distinct(d_sub$group) < 2) {
  249. return(tibble(
  250. variable = var,
  251. F_value = NA_real_,
  252. anova_p = NA_real_,
  253. eta_sq = NA_real_
  254. ))
  255. }
  257. fit <- aov(as.formula(paste(var, "~ group")), data = d_sub)
  258. aov_tab <- summary(fit)[[1]]
  260. ss_between <- aov_tab["group", "Sum Sq"]
  261. ss_total <- sum(aov_tab[, "Sum Sq"], na.rm = TRUE)
  263. tibble(
  264. variable = var,
  265. F_value = aov_tab["group", "F value"],
  266. anova_p = aov_tab["group", "Pr(>F)"],
  267. eta_sq = ss_between / ss_total
  268. )
  269. }
  271. #### update with list of variables and correct dataset ####
  272. anova_tbl <- map_dfr(HBNVars, run_anova, data = clean) ## HBN
  274. # =========================
  275. # PAIRWISE t-TESTS + HOLM
  276. # =========================
  277. run_pairwise <- function(var, data) {
  279. d_sub <- data %>% filter(!is.na(.data[[var]]))
  280. groups <- levels(droplevels(d_sub$group))
  282. if (length(groups) < 2) return(NULL)
  284. pairs <- combn(groups, 2, simplify = FALSE)
  286. res <- map_dfr(pairs, function(pair) {
  288. d_pair <- d_sub %>% filter(group %in% pair)
  290. t_res <- t.test(as.formula(paste(var, "~ group")), data = d_pair)
  292. g_val <- hedges_g_safe(
  293. x = d_pair[[var]],
  294. g = d_pair$group
  295. )
  297. tibble(
  298. variable = var,
  299. comparison = paste(pair[1], "vs", pair[2], sep = "_"),
  300. t_value = unname(t_res$statistic),
  301. p_value = t_res$p.value,
  302. hedges_g = g_val
  303. )
  304. })
  306. res %>%
  307. mutate(p_holm = p.adjust(p_value, method = "holm"))
  308. }
  310. #### update with list of variables and correct dataset ####
  311. pairwise_tbl <- map_dfr(HBNVars, run_pairwise, data = clean)
  313. # =========================
  314. # WIDE FORMAT
  315. # =========================
  316. pairwise_wide <- pairwise_tbl %>%
  317. pivot_longer(
  318. cols = c(t_value, p_holm, hedges_g),
  319. names_to = "stat",
  320. values_to = "value"
  321. ) %>%
  322. mutate(colname = paste(stat, comparison, sep = "_")) %>%
  323. select(variable, colname, value) %>%
  324. pivot_wider(names_from = colname, values_from = value)
  326. # =========================
  327. # FINAL TABLE
  328. # =========================
  329. final_table <- anova_tbl %>%
  330. left_join(pairwise_wide, by = "variable")

Marks2026_SSR_RD+ADHD.R, no license · at the source

Overview

Authors: Rebecca A. Marks1,2, Hanna Thesken2, Rachel T. Norton3, Alexander J. Kaminsky3, Carissa Mastrangelo3, Annie Cardinaux2, Karolina Wade2, Adriana M. Azor3, Andrea Takahesu Tabori3, Alysa E. Doyle4, Ellen B. Braaten4, John D. E. Gabrieli2, Joanna A. Christodoulou3
ORCID iDs: Rebecca A. Marks, Hanna Thesken, Rachel T. Norton, Alexander J. Kaminsky, Carissa Mastrangelo, Annie Cardinaux, Adriana M. Azor, Andrea Takahesu Tabori, Alysa E. Doyle, Ellen B. Braaten, John D. E. Gabrieli, Joanna A. Christodoulou
  1. Department of Human Development and Family Science, Purdue University, West Lafayette, Indiana, USA
  2. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
  3. Department of Communication Sciences and Disorders, MGH Institute of Health Professions, Boston, MA, USA
  4. Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
Institutions: Purdue University West Lafayette (United States); Massachusetts Institute of Technology (United States); MGH Institute of Health Professions (United States); Harvard University (United States); Massachusetts General Hospital (United States)
Journal: Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading, volume 30, issue 5, article 10.1080/10888438.2026.2672100
Dates: published online 18 May 2026; in print May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1080/10888438.2026.2672100 · PMID 42305424 · PMCID PMC13267917 · OpenAlex W7161551830
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: human (organism), ADHD (population), cognitive (subfield)
Methods: Connectivity, Statistics, fMRI & imaging, Machine learning
Topic: Attention Deficit Hyperactivity Disorder (Psychiatry and Mental health, Medicine), according to OpenAlex
Funding: National Institutes of Health (R01HD106122, R32HD110967, S10OD021569); NICHD NIH HHS (F32 HD110967, R01 HD106122); NIH HHS (S10 OD021569)
Citations: not cited yet (Europe PMC); 64 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

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Size: 3 files, 1 script
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Found in: the end of the paper
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: ggpubr (1 file), psych (1 file), rstatix (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers (HTTP 200)
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At the source: osf.io/puky6

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Recorded: type, language, journal, pages, dates, 13 authors, 3 funders, 51 references.

Cite

This paper

Marks, R. A., Thesken, H., Norton, R. T., Kaminsky, A. J., Mastrangelo, C., Cardinaux, A., Wade, K., Azor, A. M., Tabori, A. T., Doyle, A. E., Braaten, E. B., Gabrieli, J. D. E., & Christodoulou, J. A. (2026). Reading Disabilities With and Without Co-Occurring ADHD: No Evidence for Additive Impact on Reading Skills. Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading, 30(5), 10.1080/10888438.2026.2672100. https://doi.org/10.1080/10888438.2026.2672100

BibTeX

@article{marks2026reading,
author = {Marks, Rebecca A. and Thesken, Hanna and Norton, Rachel T. and Kaminsky, Alexander J. and Mastrangelo, Carissa and Cardinaux, Annie and Wade, Karolina and Azor, Adriana M. and Tabori, Andrea Takahesu and Doyle, Alysa E. and Braaten, Ellen B. and Gabrieli, John D. E. and Christodoulou, Joanna A.},
title = {{Reading Disabilities With and Without Co-Occurring ADHD: No Evidence for Additive Impact on Reading Skills}},
journal = {Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading},
year = {2026},
month = may,
volume = {30},
number = {5},
pages = {10.1080/10888438.2026.2672100},
publisher = {Taylor \& Francis},
issn = {1088-8438},
doi = {10.1080/10888438.2026.2672100},
url = {https://doi.org/10.1080/10888438.2026.2672100},
pmid = {42305424},
pmcid = {PMC13267917}
}

RIS

TY - JOUR
AU - Marks, Rebecca A.
AU - Thesken, Hanna
AU - Norton, Rachel T.
AU - Kaminsky, Alexander J.
AU - Mastrangelo, Carissa
AU - Cardinaux, Annie
AU - Wade, Karolina
AU - Azor, Adriana M.
AU - Tabori, Andrea Takahesu
AU - Doyle, Alysa E.
AU - Braaten, Ellen B.
AU - Gabrieli, John D. E.
AU - Christodoulou, Joanna A.
TI - Reading Disabilities With and Without Co-Occurring ADHD: No Evidence for Additive Impact on Reading Skills
T2 - Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading
J2 - Sci Stud Read
PY - 2026
DA - 2026/05/18
VL - 30
IS - 5
SP - 10.1080/10888438.2026.2672100
SN - 1088-8438
PB - Taylor & Francis
DO - 10.1080/10888438.2026.2672100
UR - https://doi.org/10.1080/10888438.2026.2672100
LA - en
ER -

CSL-JSON

{
"id": "10.1080/10888438.2026.2672100",
"type": "article-journal",
"title": "Reading Disabilities With and Without Co-Occurring ADHD: No Evidence for Additive Impact on Reading Skills",
"container-title": "Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading",
"author": [
{
"family": "Marks",
"given": "Rebecca A."
},
{
"family": "Thesken",
"given": "Hanna"
},
{
"family": "Norton",
"given": "Rachel T."
},
{
"family": "Kaminsky",
"given": "Alexander J."
},
{
"family": "Mastrangelo",
"given": "Carissa"
},
{
"family": "Cardinaux",
"given": "Annie"
},
{
"family": "Wade",
"given": "Karolina"
},
{
"family": "Azor",
"given": "Adriana M."
},
{
"family": "Tabori",
"given": "Andrea Takahesu"
},
{
"family": "Doyle",
"given": "Alysa E."
},
{
"family": "Braaten",
"given": "Ellen B."
},
{
"family": "Gabrieli",
"given": "John D. E."
},
{
"family": "Christodoulou",
"given": "Joanna A."
}
],
"container-title-short": "Sci Stud Read",
"volume": "30",
"issue": "5",
"page": "10.1080/10888438.2026.2672100",
"DOI": "10.1080/10888438.2026.2672100",
"PMID": "42305424",
"PMCID": "PMC13267917",
"ISSN": "1088-8438",
"publisher": "Taylor & Francis",
"URL": "https://doi.org/10.1080/10888438.2026.2672100",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
18
]
]
}
}

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