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Record W4411989959 · doi:10.1016/j.jsr.2025.06.026

The prevalence of benzodiazepines and other hypnotics and their impact on injury severity among older adults involved in motor vehicle collisions: a multicenter retrospective cohort study

2025· article· en· W4411989959 on OpenAlexafffund
Axel Benhamed, Marcel Émond, Shannon Erdelyi, Éric Mercier, Laurence Larouche, Herbert Chan, Pierre-Gilles Blanchard, Raoul Daoust, Christian Vaillancourt, Brian H. Rowe, Jacques Lee, Paul Atkinson, Philip J. Davis, David B. Clarke, John Taylor, Andrew MacPherson, Michael H. Parsons, Ian Wishart, Kirk Magee, Jagadish Rao, Jeffrey R. Brubacher

Bibliographic record

VenueJournal of Safety Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicOlder Adults Driving Studies
Canadian institutionsSaskatchewan HealthFoothills Medical CentreMemorial University of NewfoundlandVictoria General HospitalUniversity of British ColumbiaDalhousie UniversityUniversity of Alberta HospitalUniversity of VictoriaUniversity of SaskatchewanUniversity of AlbertaSaint John Regional HospitalVancouver General HospitalHôpital du Sacré-Cœur de MontréalUniversity of OttawaSunnybrook Health Science CentreUniversity of British Columbia HospitalCentre hospitalier universitaire de Québec
FundersTransport CanadaHealth CanadaPublic Safety Canada
KeywordsPoison controlInjury preventionMedicineHuman factors and ergonomicsOccupational safety and healthRetrospective cohort studyMulticenter studySuicide preventionMotor vehicle crashCohort studyYoung adultCohortMedical emergencyEmergency medicineGerontologyInternal medicineRandomized controlled trial

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.007
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation 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 score0.010
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.037
GPT teacher head0.436
Teacher spread0.399 · 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.

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

Citations2
Published2025
Admission routes2
Has abstractno

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