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Record W1926090064 · doi:10.1093/aje/kwv014

The Association Between Traumatic Brain Injury and Suicide: Are Kids at Risk?

2015· article· en· W1926090064 on OpenAlexafffundabout
Yvonne Richard, Bonnie Swaine, Marie‐Pierre Sylvestre, Alain Lesage, Xun Zhang, Debbie Ehrmann Feldman

Bibliographic record

VenueAmerican Journal of Epidemiology · 2015
Typearticle
Languageen
FieldPsychology
TopicSuicide and Self-Harm Studies
Canadian institutionsUniversité de Montréal
FundersCanadian Institutes of Health Research
KeywordsTraumatic brain injuryMedicineSuicide preventionPoison controlInjury preventionOccupational safety and healthAssociation (psychology)Human factors and ergonomicsMedical emergencyPsychiatryEmergency medicinePsychologyPathology

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) in late adolescence and adulthood is associated with a higher risk of suicide; however, it is unknown whether this association is also present in people who sustained a TBI during childhood. The purpose of the present study was to determine whether experiencing a TBI during childhood is a risk factor for suicide later in life and to examine whether the risk of suicide differs by sex or injury severity. A cohort of 135,703 children aged 0-17 years was identified from the Quebec population-based physician reimbursement database in 1987, and follow-up was conducted until 2008. Of the children in this cohort, 21,047 had sustained a TBI. Using a survival analysis with time-dependent indicators of TBI, we found a higher risk of suicide for people who sustained a TBI during childhood (hazard ratio (HR) = 1.49, 95% confidence interval (CI): 1.04, 2.14), adolescence (HR = 1.57, 95% CI: 1.09, 2.26), and adulthood (HR = 2.53, 95% CI: 1.79, 3.59). When compared with less severe injuries, such as concussions and cranial fractures, more severe injuries, such as intracranial hemorrhages, were associated with a higher risk of suicide (HR = 2.18 vs. 2.77, respectively). Repeated injuries were associated with higher risks of suicide in all age groups.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.105
GPT teacher head0.404
Teacher spread0.299 · 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 teacher head, not a consensus.

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

Citations35
Published2015
Admission routes3
Has abstractyes

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