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Record W2021013320 · doi:10.1111/1556-4029.12725

Toxicological Findings in Fatal Motor Vehicle Collisions in Ontario, Canada: A One‐Year Study

2015· article· en· W2021013320 on OpenAlexaffabout
Karen L. Woodall, Betty L.C. Chow, Albert E. Lauwers, Dan Cass

Bibliographic record

VenueJournal of Forensic Sciences · 2015
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsPoison controlOccupational safety and healthInjury preventionSuicide preventionHuman factors and ergonomicsForensic engineeringMedical emergencyMotor vehicle crashEngineeringAeronauticsMedicine

Abstract

fetched live from OpenAlex

Drug-impaired driving is a complex area of forensic toxicology due in part to limited data concerning the type of drugs involved and the concentrations detected. This study analyzed toxicological findings in drivers from fatal motor vehicle collisions (FMVCs) in Ontario, Canada, over a one-year period using a standardized protocol. Of the 229 cases included in the study, 56% were positive for alcohol and/or drugs. After alcohol, cannabis was the most frequently encountered substance (27%), followed by benzodiazepines (17%) and antidepressants (17%). There were differences in drugs detected by age but no marked difference in drugs detected between single and multiple FMVC's. Not all drugs detected were considered impairing either due to drug type, concentration or case history. The findings indicate the importance of comprehensive drug testing in FMVCs and highlight the need to consider a variety of factors, in addition to drug type and concentration, when assessing the role of drugs in driving impairment.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.726
Threshold uncertainty score0.757

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.044
GPT teacher head0.243
Teacher spread0.199 · 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

Citations23
Published2015
Admission routes2
Has abstractyes

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