MétaCan
Menu
← Back to cohort
Record W4407871751 · doi:10.1371/journal.pone.0316165

Longitudinal healthcare use after pediatric brain injury: A population-based birth cohort study

2025· article· en· W4407871751 on OpenAlexaffabout
Vincy Chan, Clarissa Serafine Wirianto, Robert Balogh, 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
KeywordsMedicineHealth careEmergency departmentCohortPopulationCohort studyPoison controlLongitudinal studyEmergency medicineGerontologyPediatricsPsychiatryEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Traumatic brain injury is a chronic disease with lifelong consequences. In children, it can affect developmental milestones. Longitudinal data on brain injury and long-term healthcare use is limited, with lack of clarity on social determinants of health and its effects on healthcare use. This study explores rates of healthcare use, from birth, and up to 10 years after a childhood traumatic brain injury-related healthcare visit. METHODS AND FINDINGS: This study uses a population-based birth cohort of individuals born between April 1, 2002 and March 31, 2020 from Ontario, Canada. A case cohort (TBI cohort) was created using a sample of individuals who had at least one traumatic brain injury-related healthcare visit between the ages of 0 and 4 years, inclusive (n = 26,988). Controls were generated from a sample of individuals who did not have any traumatic brain injury-related healthcare visit during the study period (n = 193,253 for emergency department visits and hospitalizations, and n = 19,313 for primary care physician visits). The primary outcome is rates of primary care physician visits, emergency department visits, and hospitalizations for each year prior to and up to 10 years after the index traumatic brain injury-related healthcare visit, calculated using standard life table methods. Rates and 95% confidence intervals were further calculated and stratified by rurality of residence, and the following Ontario Marginalization Index metrics: neighbourhood income quintile and neighbourhood racialized and newcomer populations. Rates of healthcare use remained consistently higher in the TBI cohort compared to controls both prior to and after the index TBI-related healthcare visit. Rates also varied across social determinants of health. Overall, rates were higher in males compared to females across all healthcare settings. Rates of primary care physician visits were higher among those living in urban (vs. rural) settings. However, rates of emergency department visits were higher among those living in rural (vs. urban) settings. Rates of emergency department visits and hospitalizations were higher among those living in the lowest (vs. highest) income quintile neighbourhoods. Rates of primary care physician visits were higher among those living in areas with the most (vs. least) racialized and newcomer populations. However, rates of emergency department and hospitalizations were higher among those living in areas with the least (vs. most) racialized and newcomer populations. This study is limited to change in rates of healthcare use over time and does not quantify the magnitude of these changes. CONCLUSIONS: Research on longitudinal healthcare use is needed to explore the causes of sustained and increased healthcare use post-injury, to inform opportunities for targeted health and social care interventions. Findings also suggest that a lifespan perspective is critical to understand how early life events can impact post-injury outcome.

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)0.003
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 score0.603
Threshold uncertainty score0.798

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.111
GPT teacher head0.360
Teacher spread0.249 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venuePLoS ONE→Same topicTraumatic Brain Injury Research→French-language works237,207→