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Record W4411235030 · doi:10.1371/journal.pone.0323902

Characteristics of traumatic brain injury-related healthcare visits across social determinants of health: A population-based birth cohort study

2025· article· en· W4411235030 on OpenAlexaffabout
Vincy Chan, Clarissa Serafine Wirianto, Robert Balogh, Juliet Haarbauer‐Krupa, Michael Escobar

Bibliographic record

VenuePLoS ONE · 2025
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsOntario Tech UniversityPublic Health OntarioToronto Rehabilitation InstituteUniversity of TorontoUniversity Health Network
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsMedicineTraumatic brain injuryHealth careCohortCohort studyPopulationEmergency medicineEnvironmental healthPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Traumatic brain injury is a major cause of death and disability worldwide, with almost half of new cases occurring in children, adolescents, and young adults. However, data on injury characteristics stratified by social determinants of health are scarce. This study explores severity, intent, and mechanism of traumatic brain injury sustained during childhood, adolescence, and young adulthood by social determinants of health. METHODS: This study utilizes a population-based birth cohort of births in publicly funded hospitals in Ontario, Canada, between April 1, 1992 and March 31, 2020 (n = 3,648,760). Individuals experiencing a traumatic brain injury requiring medical attention to the emergency department or acute care between April 1, 2002 and November 20, 2020 (n = 94,514) were identified using International Classification of Diseases Version 10 diagnosis codes. Social determinants of health variables included age, sex, rurality of residence, neighbourhood income quintile, and the following Ontario Marginalization Index variables: households and dwellings, material resources, and racialized and newcomer populations. The primary outcome was percentage of injuries falling under each mechanism, intent, and severity of injury category, stratified by social determinants of health variables. RESULTS: Approximately 50% of injuries were mild and 96.2% of injuries were unintentional. Injury severity and intent of injury significantly varied by social determinants of health; for example, the proportion of traumatic brain injury-related healthcare visits for moderate/severe and intentional injuries was highest in areas with the lowest income quintile (13.3% and 6.1%, respectively), lowest households and dwellings stability (12.2% and 5.7%, respectively), lowest material resources (12.8% and 6.0% respectively), and highest racialized and newcomer populations (13.5% and 4.5% respectively). The percentage of traumatic brain injury-related healthcare visits for a sports-related injury significantly varied by social determinants of health; for example, the proportion of traumatic brain injury-related healthcare visits for sports-related injuries was highest among males (45.5%), those living rural areas (44.0%), and those living in areas with the highest income (47.2%), highest households and dwellings stability (44.0%), highest material resources (45.8%), and lowest racialized and newcomer populations (43.4%). CONCLUSIONS: Characteristics of traumatic brain injury-related healthcare visits vary based on social determinants of health. Targeted prevention of traumatic brain injury beyond the sports settings, including fall prevention among young children, are encouraged, and guidelines to identify and address traumatic brain injury outside of the sports setting must be developed to support early intervention of traumatic brain injury across social determinants of health.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.787

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.124
GPT teacher head0.417
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes2
Has abstractyes

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