Observable signs of concussion in professional slap fighting using established video review protocols for professional sports: frequency, predictors, and implications for public concussion recognition awareness.

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[1] § Results › Frequency of observable signs of concussion ↔ peerj-14-21623-s004.rmd, lines 828–856 · score 0.51 · vacant look, motor incoordination, blank, strikes, class

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The authors' code

R Markdown · 880 lines · 25 KB · no license · 1 match

---
title: "PowerSlap"
author: "Corey Stewart"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(ggplot2)
library(gtsummary)
library(scales)
library(lme4)
library(broom)
library(broom.mixed)
library(officer)
DATA = read_csv("PowerSlap.csv")
STRIKERS = read_csv("Strikers.csv", na = "NA")
```
```{r Add Striker Atrributes (Concusions)}
DATA <- DATA %>%
group_by(Event, Match) %>%
arrange(Slap, .by_group = TRUE) %>%
mutate(
Striker_PotentialConcussion = FALSE,
Striker_Concussion_Total = 0
) %>%
group_modify(~ {
concussed <- character(0)
totals <- list()
for (i in seq_len(nrow(.x))) {
striker <- .x$Striker[i]
slappee <- .x$Slappee[i]
if (striker %in% concussed) {
.x$Striker_PotentialConcussion[i] <- TRUE
}
totals[[striker]] <- (totals[[striker]] %||% 0) +
as.integer(.x$Striker_PotentialConcussion[i])
.x$Striker_Concussion_Total[i] <- totals[[striker]]
if (.x$PotentialConcussion[i]) {
concussed <- union(concussed, slappee)
}
}
.x
}) %>%
ungroup()
```
```{r Data Maneuvering}
STRIKERS$Division <- factor(
STRIKERS$Division,
levels = c("Bantamweight", "Featherweight",
"Lightweight", "Welterweight", "Middleweight",
"Light Heavyweight", "Heavyweight", "Super Heavyweight")
)
NFL <- DATA %>%
select(Event, Match, Slappee, Cons_Motor, Cons_Posturing, Cons_Blank, Cons_Loss, Cons_Behavior, Cons_Slow, Legal_Slap_Num)
Rugby <- DATA %>%
select(Event, Match, Slappee, Cons_Motor, Cons_Posturing, Cons_Blank, Cons_Loss, Cons_Suspected_Loss, Legal_Slap_Num)
International <- DATA %>%
select(Event, Match, Slappee, Cons_Motor, Cons_Posturing, Cons_Floppy, Cons_Blank, Legal_Slap_Num)
Rugby <- Rugby %>%
mutate(PotentialConcussion = if_any(starts_with("Cons_"), ~ .x == TRUE))
NFL <- NFL %>%
mutate(PotentialConcussion = if_any(starts_with("Cons_"), ~ .x == TRUE))
International <- International %>%
mutate(PotentialConcussion = if_any(starts_with("Cons_"), ~ .x == TRUE))
knockoutData <- DATA %>%
select(Event,Match,Result) %>%
distinct(Event,Match,Result) %>%
arrange(Event,Match) %>%
mutate(Result = fct_recode(Result,
"Decision" = "Split Decision",
"Decision" = "Unanimous Decision"))
knockoutSummary <- knockoutData %>%
group_by(Result) %>%
summarise(numResult = n(), .groups = "drop")
SlapCounts <- DATA %>%
group_by(Event, Match) %>%
summarise(numSlaps = n(), .groups = "drop")
overall_median <- median(SlapCounts$numSlaps, na.rm = TRUE)
criteria_summary <- bind_rows(
NFL %>% count(PotentialConcussion) %>% mutate(Criteria = "NFL"),
Rugby %>% count(PotentialConcussion) %>% mutate(Criteria = "Rugby"),
International %>% count(PotentialConcussion) %>% mutate(Criteria = "International")
)
#criteria_summary <- bind_rows(
# NFL %>% summarise(Percent = mean(PotentialConcussion), .groups = "drop") %>% mutate(Criteria = "NFL"),
#Rugby %>% summarise(Percent = mean(PotentialConcussion), .groups = "drop") %>% mutate(Criteria = "Rugby"),
#International %>% summarise(Percent = mean(PotentialConcussion), .groups = "drop") %>% mutate(Criteria = "International")
#)
DATA_with_class <- DATA %>%
left_join(STRIKERS %>% select(ID, Division),
by = c("Slappee" = "ID"))
DATA_with_class <- DATA_with_class %>%
left_join(STRIKERS %>% select(ID, `Reach(cm)`, `Height(cm)`),
by = c("Striker" = "ID")) %>%
rename(Reach = `Reach(cm)`,
Height = `Height(cm)`)
DATA_with_class %>%
group_by(Division) %>%
summarise(
Slaps = n(),
Signs = sum(PotentialConcussion),
Rate = mean(PotentialConcussion),
.groups = "drop"
)
match_summary <- DATA_with_class %>%
group_by(Event, Match, Division) %>%
summarise(Rate = mean(PotentialConcussion), .groups = "drop")
match_summary %>%
group_by(Division) %>%
summarise(
MeanRate = mean(Rate),
MedianRate = median(Rate),
Matches = n(),
.groups = "drop"
)
striker_summary <- STRIKERS %>%
group_by(Sex) %>%
summarise(
Count = n(),
Avg_Weight = mean(`Weight(kg)`),
Stan_Dev = sd(`Weight(kg)`),
)
```
```{r Descriptive Analysis}
concussion_median <- median(match_summary$Rate)
avgAge <- mean(STRIKERS$Age)
sdAge <- sd(STRIKERS$Age)
avgHeight <- mean(STRIKERS$`Height(cm)`)
sdHeight <- sd(STRIKERS$`Height(cm)`)
avgReach <- mean(STRIKERS$`Reach(cm)`, na.rm = TRUE)
sdReach <- sd(STRIKERS$`Reach(cm)`, na.rm = TRUE)
avgWeight <- mean(STRIKERS$`Weight(kg)`)
sdWeight <- sd(STRIKERS$`Weight(kg)`)
signs <- c(
"Cons_Motor",
"Cons_Posturing",
"Cons_Floppy",
"Cons_Blank",
"Cons_Loss",
"Cons_Suspected_Loss",
"Cons_Slow",
"Cons_Behavior",
"Cons_Facial",
"Cons_Fencing"
)
nflSigns <- c(
"Cons_Motor",
"Cons_Posturing",
"Cons_Blank",
"Cons_Loss",
"Cons_Behavior",
"Cons_Slow"
)
rugbySigns <- c(
"Cons_Motor",
"Cons_Posturing",
"Cons_Blank",
"Cons_Loss",
"Cons_Suspected_Loss"
)
internationalSigns <- c(
"Cons_Motor",
"Cons_Posturing",
"Cons_Floppy",
"Cons_Blank"
)
overall_counts <- DATA %>%
summarise(
slaps = n(),
across(
all_of(signs), ~ sum(.x, na.rm = TRUE))
) %>%
pivot_longer(
cols = all_of(signs),
names_to = "sign",
values_to = "count"
) %>%
mutate(
pct = (count / slaps) * 100
)
NFL_counts <- NFL %>%
summarise(
slaps = n(),
across(
all_of(nflSigns), ~ sum(.x, na.rm = TRUE))
) %>%
pivot_longer(
cols = all_of(nflSigns),
names_to = "sign",
values_to = "count"
) %>%
mutate(
pct = (count / slaps) * 100
)
Rugby_counts <- Rugby %>%
summarise(
slaps = n(),
across(
all_of(rugbySigns), ~ sum(.x, na.rm = TRUE))
) %>%
pivot_longer(
cols = all_of(rugbySigns),
names_to = "sign",
values_to = "count"
) %>%
mutate(
pct = (count / slaps) * 100
)
International_counts <- International %>%
summarise(
slaps = n(),
across(
all_of(internationalSigns), ~ sum(.x, na.rm = TRUE))
) %>%
pivot_longer(
cols = all_of(internationalSigns),
names_to = "sign",
values_to = "count"
) %>%
mutate(
pct = (count / slaps) * 100
)
strikerCounts <- DATA %>%
group_by(Slappee) %>%
summarise(
across(
all_of(signs), ~ sum(.x, na.rm = TRUE)),
.groups = "drop"
)
classCounts <- DATA_with_class %>%
group_by(Division) %>%
summarise(
slaps = n(),
across(
all_of(signs), ~ sum(.x, na.rm = TRUE)),
.groups = "drop"
) %>%
pivot_longer(
cols = all_of(signs),
names_to = "sign",
values_to = "count"
) %>%
mutate(
pct = (count / slaps) * 100
)
mostCommonOverall <- overall_counts %>%
slice_max(pct, n = 3)
mostCommonByClass <- classCounts %>%
group_by(Division) %>%
slice_max(pct, n = 3)
# concussionByClass <- DATA_with_class %>%
# group_by(Division) %>%
# summarise()
run_all_metrics <- function(df) {
receivingParticipants <- df %>%
group_by(Event, Match, Slappee) %>%
summarize(
receivedAny = n() > 0,
anySign = any(PotentialConcussion),
.groups = "drop"
)
participantLevel <- receivingParticipants %>%
summarize(
nReceiving = n(),
nWithSign = sum(anySign),
propWithSign = (nWithSign / nReceiving) * 100
)
receivingCombatants <- df %>%
group_by(Slappee) %>%
summarize(
matchesAsSlapee = n_distinct(paste(Event, Match)),
slaps = n(),
sign = any(PotentialConcussion),
.groups = "drop"
)
combatantLevel <- receivingCombatants %>%
summarise(
combatants = n(),
withSign = sum(sign),
propWithSign = (withSign / combatants) * 100
)
slapLevel <- df %>%
summarise(
slaps = n(),
slapsWithSign = sum(PotentialConcussion, na.rm = TRUE),
slapsWithoutSign = slaps - slapsWithSign,
propWithSign = (slapsWithSign / slaps) * 100
)
matchLevel <- df %>%
group_by(Event, Match) %>%
summarise(
anySign = any(PotentialConcussion),
.groups = "drop"
) %>%
summarise(
matches = n(),
matchWithSign = sum(anySign),
matchWithoutSign = matches - matchWithSign,
percWithSign = (matchWithSign / matches) * 100
)
eventMatchCounts <- df %>%
group_by(Event, Match) %>%
summarise(
anySign = any(PotentialConcussion, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(Event) %>%
summarise(
totalMatches = n(),
matchesWithSign = sum(anySign),
propMatchesWithSign = matchesWithSign / totalMatches,
.groups = "drop"
)
eventMatchSummary <- eventMatchCounts %>%
summarise(
events = n(),
meanMatches = mean(totalMatches),
meanMatchesWithSign = mean(matchesWithSign),
sdMatchesWithSign = sd(matchesWithSign),
medianMatchesWithSign = median(matchesWithSign),
minMatchesWithSign = min(matchesWithSign),
maxMatchesWithSign = max(matchesWithSign),
meanPropMatchesWithSign = mean(propMatchesWithSign),
sdPropMatchesWithSign = sd(propMatchesWithSign),
medianPropMatchesWithSign = median(propMatchesWithSign),
minPropMatchesWithSign = min(propMatchesWithSign),
maxPropMatchesWithSign = max(propMatchesWithSign)
)
eventLevel <- df %>%
group_by(Event) %>%
summarise(
anySign = any(PotentialConcussion),
.groups = "drop"
) %>%
summarise(
matches = n(),
matchWithSign = sum(anySign),
matchWithoutSign = matches - matchWithSign,
percWithSign = (matchWithSign / matches) * 100
)
boutMeta <- df %>%
group_by(Event, Match) %>%
summarise(
slaps = n(),
receivers = n_distinct(Slappee)
)
eligibleBouts <- boutMeta %>%
filter(slaps > 1,
receivers == 2)
participantSignsPerBout <- df %>%
semi_join(eligibleBouts, by = c("Event", "Match")) %>%
group_by(Event, Match, Slappee) %>%
summarise(
sign = any(PotentialConcussion),
.groups = "drop"
)
boutSigns <- participantSignsPerBout %>%
group_by(Event, Match) %>%
summarise(
signsPerBout = sum(sign),
.groups = "drop"
)
oneConcussionPerBout <- boutSigns %>%
summarise(
bouts = n(),
boutsWithOne = sum(signsPerBout == 1),
propWithOne = (boutsWithOne / bouts) * 100
)
twoConcussionPerBout <- boutSigns %>%
summarise(
bouts = n(),
boutsWithTwo = sum(signsPerBout == 2),
propWithTwo = (boutsWithTwo / bouts) * 100
)
multipleConcussions <- df %>%
semi_join(eligibleBouts, by = c("Event", "Match")) %>%
group_by(Event, Match, Slappee, Legal_Slap_Num) %>%
summarise(
anySign = any(PotentialConcussion, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(Event, Match, Slappee) %>%
summarise(
roundSigns = sum(anySign),
multipleRounds = roundSigns >= 2,
.groups = "drop"
) %>%
group_by(Event, Match) %>%
summarise(
multiRoundSigns = sum(multipleRounds),
noOne = multiRoundSigns == 0,
onlyOne = multiRoundSigns == 1,
both = multiRoundSigns == 2,
.groups = "drop"
) %>%
summarise(
matches = n(),
matchesWithNone = sum(noOne),
matchesWithOne = sum(onlyOne),
matchesWithTwo = sum(both)
)
return(list(receivingParticipants = receivingParticipants, participantLevel = participantLevel, receivingCombatants = receivingCombatants, combatantLevel = combatantLevel, slapLevel = slapLevel, eventLevel = eventLevel, eventMatchCounts = eventMatchCounts, eventMatchSummary = eventMatchSummary, matchLevel = matchLevel, boutMeta = boutMeta, eligibleBouts = eligibleBouts, participantSignsPerBout = participantSignsPerBout, boutSigns = boutSigns, oneConcussionPerBout = oneConcussionPerBout, twoConcussionPerBout = twoConcussionPerBout, multipleConcussions = multipleConcussions))
}
Overall_results <- run_all_metrics(DATA)
NFL_results <- run_all_metrics(NFL)
Rugby_results <- run_all_metrics(Rugby)
International_results <- run_all_metrics(International)
TableData <- DATA %>%
select(Event, Match, all_of(signs), PotentialConcussion)
DATA$PotentialConcussion <- factor(
DATA$PotentialConcussion,
levels = c(FALSE, TRUE),
labels = c("Absent", "Present")
)
DATA_with_class$PotentialConcussion <- factor(
DATA_with_class$PotentialConcussion,
levels = c(FALSE, TRUE),
labels = c("Absent", "Present")
)
criteria_summary$PotentialConcussion <- factor(
criteria_summary$PotentialConcussion,
levels = c(FALSE, TRUE),
labels = c("Absent", "Present")
)
```
```{r Descriptive Statistics}
ggplot(DATA, aes(x = factor(Event), fill = PotentialConcussion)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = percent) +
labs(x = "Event", y = "Percentage of Strikes", fill = "Sign of Concussion", title = "Strikes with at Least One Observable Sign of Concussion By Event") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14)
)
DATA %>%
count(PotentialConcussion) %>%
ggplot(aes(x = "", y = n, fill = PotentialConcussion)) +
geom_col(width = 1) +
coord_polar(theta = "y") +
geom_text(aes(label = scales::percent(n/sum(n))),
position = position_stack(vjust = 0.5),
size = 8) +
labs(title = "Strikes with at Least One Observable Sign of Concussion", fill = "Sign of Concussion") +
theme_void() +
theme(
plot.title = element_text(size = 14, hjust = 0.5),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14)
)
ggplot(knockoutData, aes(x = Result, fill = Result)) +
geom_bar() +
labs(y = "Number of Bouts", title = "Number of Bouts per Decision") +
theme_minimal() +
theme(legend.position = "none")
ggplot(SlapCounts, aes(x = factor(Event), y = numSlaps)) +
geom_boxplot(fill = "skyblue", alpha = 0.6, outlier.shape = NA) +
geom_jitter(width = 0.2,height = 0, alpha = 0.7, color = "darkblue") +
geom_hline(yintercept = overall_median,
linetype = "dashed", color = "red", size = 1) +
scale_y_continuous(breaks = seq(0, 12, by = 2)) +
labs(x = "Event", y = "Total Strikes per Bout",
title = "Distribution of Bout Lengths (Strikes) by Event") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14)
)
ggplot(criteria_summary, aes(x = Criteria, y = n, fill = PotentialConcussion)) +
geom_col(position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(x = "Criteria", y = "Percentage of Strikes",
title = "Observable Signs of Concussion by Criteria", fill = "Sign of Concussion") +
theme_minimal() +
theme(
plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14)
)
ggplot(DATA_with_class, aes(x = Division, fill = PotentialConcussion)) +
geom_bar(position = "fill") +
scale_y_continuous(labels = scales::percent) +
labs(x = "Weight Class", y = "Percentage",
title = "Strikes Resulting in Concussion Sign by Weight Class", fill = "Sign of Concussion") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 10),
plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text.y = element_text(size = 14))
ggplot(match_summary, aes(x = Division, y = Rate)) +
geom_boxplot(fill = "skyblue", alpha = 0.6, outlier.shape = NA) +
geom_jitter(width = 0.2, height = 0, alpha = 0.7, color = "darkblue", size = 2) +
geom_hline(yintercept = concussion_median,
linetype = "dashed", color = "red", size = 1) +
scale_y_continuous(labels = scales::percent) +
labs(x = "Weight Class", y = "Rate of Concussion Signs in a Given Bout",
title = "Distribution of Concussion Signs per Bout by Weight Class") +
theme_minimal() +
theme(plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text.y = element_text(size = 14),
axis.text.x = element_text(angle = 45, hjust = 1, size = 10))
ggplot(STRIKERS, aes(x = Age)) +
geom_histogram(binwidth = 5, fill = "skyblue", color = "black", alpha = 0.7) +
geom_vline(xintercept = avgAge,
linetype = "dashed", color = "red", size = 1) +
labs(x = "Age (years)", y = "Number of Strikers",
title = "Distribution of Striker Ages") +
theme_minimal() +
theme(plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14))
ggplot(STRIKERS, aes(x = Sex, fill = Sex)) +
geom_bar() +
theme_minimal() +
labs(y = "Number of Strikers") +
theme(legend.position = "none")
ggplot(STRIKERS, aes(x = Division, fill = Division)) +
geom_bar() +
theme_minimal() +
labs(y = "Number of Strikers", x = "Weight Class", title = "Number of Strikers per Weight Class") +
theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "none",
plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14))
ggplot(STRIKERS, aes(x = `Reach(cm)`)) +
geom_histogram(binwidth = 5, fill = "skyblue", color = "black", alpha = 0.7) +
geom_vline(xintercept = avgReach,
linetype = "dashed", color = "red", size = 1) +
labs(x = "Reach (cm)", y = "Number of Strikers",
title = "Distribution of Striker Reach") +
theme_minimal() +
theme(plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14))
ggplot(STRIKERS, aes(x = `Height(cm)`)) +
geom_histogram(binwidth = 5, fill = "skyblue", color = "black", alpha = 0.7) +
geom_vline(xintercept = avgHeight,
linetype = "dashed", color = "red", size = 1) +
labs(x = "Height (cm)", y = "Number of Strikers",
title = "Distribution of Striker Height") +
theme_minimal() +
theme(plot.title = element_text(size = 14, hjust = 0.5),
axis.title = element_text(size = 14),
axis.text = element_text(size = 14))
```
```{r Modeling Mixed Effects}
DATA_with_class$Reach_z <- scale(DATA_with_class$Reach)
mixed_model <- glmer(PotentialConcussion ~ Division + Cons_Grimmace + Cons_Jaw + Cons_Cheeks + Cons_Turn + Cons_Lean + Cons_Knees + Striker_PotentialConcussion + Striker_Concussion_Total + Reach_z + (1 | Match) + (1 | Striker) + (1 | Slappee),
data = DATA_with_class,
family = binomial)
exp(cbind(OR = fixef(mixed_model), confint(mixed_model, parm = "beta_", method = "Wald")))
results_mixed <- tidy(mixed_model, effects = "fixed", conf.int = TRUE, exponentiate = TRUE) %>%
filter(term != "(Intercept)")
results_mixed <- results_mixed %>%
mutate(term = recode(term,
"Striker_PotentialConcussionTRUE" = "Previous Sign of Concussion",
"Striker_Concussion_Total" = "Total Prior Signs of Concussion",
"Reach_z" = "Reach (z-score)",
"DivisionLightweight" = "Lightweight",
"DivisionWelterweight" = "Welterweight",
"DivisionMiddleweight" = "Middleweight",
"DivisionLight Heavyweight" = "Light Heavyweight",
"DivisionHeavyweight" = "Heavyweight",
"DivisionSuper Heavyweight" = "Super Heavyweight",
"Cons_GrimmaceTRUE" = "Pre-Slap Grimace",
"Cons_JawTRUE" = "Clenched Jaw",
"Cons_CheeksTRUE" = "Inflated Cheeks",
"Cons_TurnTRUE" = "Turned Before Impact",
"Cons_LeanTRUE" = "Leaning on Table",
"Cons_KneesTRUE" = "Knees Bent"
))
results_mixed$term <- factor(results_mixed$term, levels = c(
"Lightweight",
"Welterweight",
"Middleweight",
"Light Heavyweight",
"Heavyweight",
"Super Heavyweight",
"Pre-Slap Grimace",
"Clenched Jaw",
"Inflated Cheeks",
"Turned Before Impact",
"Leaning on Table",
"Knees Bent",
"Previous Sign of Concussion",
"Total Prior Signs of Concussion",
"Reach (z-score)"
))
results_mixed$term <- forcats::fct_rev(results_mixed$term)
write.csv(results_mixed, "mixed_model_results.csv", row.names = FALSE)
```
```{r Plotting models}
ggplot(results_mixed, aes(x = term, y = estimate, ymin = conf.low, ymax = conf.high)) +
geom_pointrange(color = "darkgreen") +
geom_hline(yintercept = 1, linetype = "dashed", color = "red") +
coord_flip() +
geom_text(
aes(label = paste0("p = ", formatC(p.value, format = "f", digits = 3))),
hjust = -1.2,
vjust = 1.2,
size = 3.5) +
labs(
x = "Pre-Slap Indicators",
y = "Odds Ratio (95% CI)",
title = "Predictors of Concussion Signs in Power Slap"
) +
theme_minimal(base_size = 12) +
scale_y_continuous(limits = c(0, 5))
```
```{r Regression Table}
t2 <- tbl_regression(
mixed_model,
exponentiate = TRUE,
label = list(
Striker_PotentialConcussion ~ "Striker Sign of Concussion",
Striker_Concussion_Total ~ "Total Prior Striker Signs of Concussion",
Reach_z ~ "Reach (z-score)",
Cons_Grimmace ~ "Pre-Slap Grimace",
Cons_Jaw ~ "Clenched Jaw",
Cons_Cheeks ~ "Inflated Cheeks",
Cons_Turn ~ "Turned Before Impact",
Cons_Lean ~ "Leaning on Table",
Cons_Knees ~ "Knees Bent",
Division ~ "Weight Class"
)
) %>%
bold_labels() %>%
remove_row_type(type = "reference")
t2 %>%
as_flex_table() %>%
flextable::save_as_docx(path = "Table2.docx")
t2
```
```{r Descriptive Stats table for participants}
t1 <- tbl_summary(
data = STRIKERS %>%
select(-FirstName, -LastName, -ID, -`Height(in)`, -`Weight(lbs)`, -`Reach(in)`),
by = Sex,
type = all_continuous() ~ "continuous2",
statistic = list(
all_continuous() ~ c(
"{mean} ({sd})",
"{median} ({p25}-{p75})",
"{min}–{max}"
),
all_categorical() ~ "{n} ({p}%)"
),
digits = list(Age ~ 1, `Height(cm)` ~ 1, `Reach(cm)` ~ 1, `Weight(kg)` ~ 1, Division ~ 0),
label = list(
Age ~ "Age (years)",
`Height(cm)` ~ "Height (cm)",
`Reach(cm)` ~ "Reach (cm)",
`Weight(kg)` ~ "Weight (kg)",
Division ~ "Weight Class (Upper Limit)"
),
missing = "no"
) %>%
add_overall(last = FALSE) %>%
add_stat_label(
label = all_continuous() ~ c(
"Mean (SD)",
"Median (Q1-Q3)",
"Range" # A placeholder label to modify later
),
location = "row") %>%
modify_header(label = "**Variable**") %>%
modify_footnote(all_stat_cols() ~ "Mean (SD), Median (Q1-Q3), and Range shown for continuous variables") %>%
bold_labels()
t1 %>%
as_flex_table() %>%
flextable::save_as_docx(path = "Table1.docx")
t1
```
```{r Descriptive Stats for slaps}
signLabels <- c(
"Motor Incoordination",
"Tonic Posturing",
"No Protective Action - Floppy",
"Blank/Vacant Look",
"Any Loss of Consciousness",
"Suspected Loss of Consciousness",
"Slow to Get Up",
"Behavior Change",
"Facial Injury",
"Fencing Response"
)
CountTable <- DATA %>%
select(all_of(signs)) %>%
tbl_summary(
digits = all_categorical() ~ 0,
label = list(
Cons_Motor ~ "Motor Incoordination",
Cons_Posturing ~ "Tonic Posturing",
Cons_Floppy ~ "No Protective Action - Floppy",
Cons_Blank ~ "Blank/Vacant Look",
Cons_Loss ~ "Any Loss of Consciousness",
Cons_Suspected_Loss ~ "Suspected Loss of Consciousness",
Cons_Slow ~ "Slow to Get Up",
Cons_Behavior ~ "Behavior Change",
Cons_Facial ~ "Facial Injury",
Cons_Fencing ~ "Fencing Response"
),
statistic = all_categorical() ~ "{n} ({p}%)"
) %>%
modify_header(all_stat_cols() ~ "**Strikes = {n}**")
CountTable %>%
as_flex_table() %>%
flextable::save_as_docx(path = "Table3.docx")
CountTable
```
```{r}
mostCommonTable <- DATA_with_class %>%
select(all_of(signs), Division) %>%
tbl_summary(
by = Division,
digits = all_categorical() ~ 0,
label = list(
Cons_Motor ~ "Motor Incoordination",
Cons_Posturing ~ "Tonic Posturing",
Cons_Floppy ~ "No Protective Action - Floppy",
Cons_Blank ~ "Blank/Vacant Look",
Cons_Loss ~ "Any Loss of Consciousness",
Cons_Suspected_Loss ~ "Suspected Loss of Consciousness",
Cons_Slow ~ "Slow to Get Up",
Cons_Behavior ~ "Behavior Change",
Cons_Facial ~ "Facial Injury",
Cons_Fencing ~ "Fencing Response"
),
statistic = all_categorical() ~ "{n} ({p}%)"
) %>%
modify_header(all_stat_cols() ~ " **{level}** \nStrikes = {n}") %>%
modify_table_body(~ .x %>% select(-stat_1))
mostCommonTable %>%
as_flex_table() %>%
flextable::save_as_docx(path = "Table5.docx")
mostCommonTable
```
```{r}
concussionByClass <- DATA_with_class %>%
select(Division, PotentialConcussion) %>%
tbl_summary(
by = Division
)
concussionByClassMatch <- DATA_with_class %>%
group_by(Event, Match, Division) %>%
summarise(
any_sign = any(PotentialConcussion),
.groups = "drop"
) %>%
select(Division, any_sign) %>%
tbl_summary(
by = Division
)
concussionByClass
concussionByClassMatch
```

peerj-14-21623-s004.rmd, no license · at the source

Overview

Authors: Corey J. Stewart^1,2,3, Erik B. Philipson⁴, Jeremy R. Caspell^5,6, Keilin Gorman⁷, Rory A. Marshall^1,2, Jonathan Lifshitz^1,2,3,8

Institutions: University of Michigan (United States); Michigan Medicine (United States); Washington State University Spokane (United States); Alberta Health Services (Canada); Thompson Rivers University (Canada); University of British Columbia, Okanagan Campus (Canada); VA Ann Arbor Healthcare System (United States)

Journal: PeerJ, volume 14, article e21623

Dates: received 3 March 2026; accepted 30 June 2026; published online 5 August 2026

Type: Research article · Language: English

License: CC BY

Identifiers: DOI 10.7717/peerj.21623 · PMID 42571483 · PMCID PMC13452423 · OpenAlex W7172525865

Open access: gold, a free copy (OpenAlex)

Status: code verified

Categories: human (organism), traumatic brain injury (population), clinical / translational (subfield)

Methods: Machine learning

Keywords: Power slap, Traumatic brain injury, Concussion, Signs of concussion, Slap fighting

MeSH: Athletic Injuries*, Brain Concussion*, Clinical Protocols*, Sports*, Symptom Assessment*, Adult, Female, Humans, Male, Video Recording (* major topic)

Journal subjects: Neuroscience, Rehabilitation, Sports Medicine

Topic: Traumatic Brain Injury Research (Epidemiology, Medicine), according to OpenAlex

Funding: A Rackham Pre-Candidate Research Grant from the University of Michigan; philanthropic donation

Citations: not cited yet (Europe PMC); 33 references in the paper

Abstract

Introduction: Concussion is a traumatic brain injury (TBI) with potential short- and long-term neurological consequences. Recognition of objective concussion signs remains limited. In slap fighting, a combat event involving the exchange of defenseless head contact, concussion signs are visible. The study objective was to examine the frequency of observable signs of concussion among professional slap fighters based on established video review protocols and determine whether participant characteristics predict concussion signs.

Methods: Three independent reviewers evaluated publicly available video of professional slap fighting events (Power Slap™; 2023–2024) against the National Football League Concussion Protocol, the World Rugby Head Injury Assessment, and International Consensus Guidelines for observable signs of concussion. Signs were recorded for each strike and aggregated across rounds, matches, events, and combatants. Mixed effects logistic regression evaluated associations among pre-slap characteristics and the likelihood of observing a sign of concussion following a strike.

Results: Combatants (60 male, three female) competed across 62 matches in six Power Slap™ events. Of the 61 combatants who received a strike, 46 exhibited at least one sign of concussion. Overall, 29% (82/280) of strikes resulted in at least one observable sign of concussion, with lower, identical results across protocol guidelines (24%; 66/280). Signs of concussion were observed in 90% (56/62) of matches. Motor incoordination (23%; 64/280), blank/vacant look (15%; 41/280), and slow to get up (14%; 40/280) were the most frequently observed signs.

Conclusion: Observable signs of concussion from professional slap fighting occurred in one-third of strikes and the majority of matches.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

supp:PMC13452423/peerj-14-21623-s004.rmd

License: none: the authors keep all their rights

State: the link answers, verified on 27 September 2026

Evidence: files inventoried

Languages: R (1)

Size: 1 file, 1 script

Software Heritage: not checked

Found in: the supplementary material

Holds: 1 notebook

Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation

Tools: broom (1 file), ggplot2 (1 file), lme4 (1 file), tidyverse (1 file)

Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)

27 September 2026: the link answers (HTTP 200)

1 file

peerj-14-21623-s004.rmd, R, 880 lines, 1 match

At the source: pmc-oa-opendata.s3.amazonaws.com/PMC13452423.1/peerj-14-21623-s004.rmd

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:

1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
1 script, each with its path and the digest of its content;
1 match 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.

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, pages, dates, 6 authors, 5 keywords, 10 MeSH terms, 2 funders, 23 references.

Cite

This paper

Stewart, C. J., Philipson, E. B., Caspell, J. R., Gorman, K., Marshall, R. A., & Lifshitz, J. (2026). Observable signs of concussion in professional slap fighting using established video review protocols for professional sports: frequency, predictors, and implications for public concussion recognition awareness. PeerJ, 14, e21623. https://doi.org/10.7717/peerj.21623

BibTeX

@article{stewart2026observable, author = {Stewart, Corey J. and Philipson, Erik B. and Caspell, Jeremy R. and Gorman, Keilin and Marshall, Rory A. and Lifshitz, Jonathan}, title = {{Observable signs of concussion in professional slap fighting using established video review protocols for professional sports: frequency, predictors, and implications for public concussion recognition awareness}}, journal = {PeerJ}, year = {2026}, month = aug, volume = {14}, pages = {e21623}, publisher = {PeerJ, Inc}, issn = {2167-8359}, doi = {10.7717/peerj.21623}, url = {https://doi.org/10.7717/peerj.21623}, pmid = {42571483}, pmcid = {PMC13452423} }

RIS

TY - JOUR AU - Stewart, Corey J. AU - Philipson, Erik B. AU - Caspell, Jeremy R. AU - Gorman, Keilin AU - Marshall, Rory A. AU - Lifshitz, Jonathan TI - Observable signs of concussion in professional slap fighting using established video review protocols for professional sports: frequency, predictors, and implications for public concussion recognition awareness T2 - PeerJ J2 - PeerJ PY - 2026 DA - 2026/08/05 VL - 14 SP - e21623 SN - 2167-8359 PB - PeerJ, Inc DO - 10.7717/peerj.21623 UR - https://doi.org/10.7717/peerj.21623 LA - en ER -

CSL-JSON

{ "id": "10.7717/peerj.21623", "type": "article-journal", "title": "Observable signs of concussion in professional slap fighting using established video review protocols for professional sports: frequency, predictors, and implications for public concussion recognition awareness", "container-title": "PeerJ", "author": [ { "family": "Stewart", "given": "Corey J." }, { "family": "Philipson", "given": "Erik B." }, { "family": "Caspell", "given": "Jeremy R." }, { "family": "Gorman", "given": "Keilin" }, { "family": "Marshall", "given": "Rory A." }, { "family": "Lifshitz", "given": "Jonathan" } ], "container-title-short": "PeerJ", "volume": "14", "page": "e21623", "DOI": "10.7717/peerj.21623", "PMID": "42571483", "PMCID": "PMC13452423", "ISSN": "2167-8359", "publisher": "PeerJ, Inc", "URL": "https://doi.org/10.7717/peerj.21623", "language": "en", "issued": { "date-parts": [ [ 2026, 8, 5 ] ] } }

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