MétaCan
Menu
Back to cohort
Record W1978711333 · doi:10.1001/jama.2013.6750

Prevalence and Correlates of Traumatic Brain Injuries Among Adolescents

2013· article· en· W1978711333 on OpenAlexafffundabout
G. Ilie, Angela Boak, Edward M. Adlaf, Mark Asbridge, Michael D. Cusimano

Bibliographic record

VenueJAMA · 2013
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsSt. Michael's Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineTraumatic brain injuryInjury preventionOccupational safety and healthPoison controlSuicide preventionHuman factors and ergonomicsMedical emergencyPsychiatryEmergency medicinePediatricsPathology

Abstract

fetched live from OpenAlex

Traumatic brain injury (TBI) among adolescents has been identified as an important health priority. However, studies of TBI among adolescents in large representative samples are lacking.This information is important to the planning and evaluation of injury prevention efforts, particularly because even minor TBI may have important adverse consequences. We describe the prevalence of TBI, the mechanisms of injury, and adverse correlates in a large representative sample of adolescents living in Ontario, Canada. Language: en

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)0.000
metaresearch head score (Gemma)0.001
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 score0.942
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.032
GPT teacher head0.309
Teacher spread0.278 · 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

Citations103
Published2013
Admission routes3
Has abstractyes

Explore more

Same venueJAMASame topicTraumatic Brain Injury ResearchFrench-language works237,207