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Record W268589906

Examination of Traumatic Brain Injured Drivers’ Behavioural Reactions to Simulated Complex Roadway Events

2008· article· en· W268589906 on OpenAlexaff
Arne Stinchcombe, Stéphanie Yamin, Andrée-Ann Cyr, Sylvain Gagnon, Shawn Marshall, Malcolm Man-Son Hing, Hillel M. Finestone

Bibliographic record

VenueAdvances in transportation studies · 2008
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsTraumatic brain injuryPoison controlPhysical medicine and rehabilitationInjury preventionPsychologyCrashHuman factors and ergonomicsOccupational safety and healthCognitionMedicineMedical emergencyNeuroscienceComputer sciencePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

This paper will discuss how traumatic brain injured (TBI) drivers exhibit a greater risk for traffic violations and motor vehicle crashes resulting from lasting behavioral and cognitive impairments. The purpose of this investigation is to examine the behavioral reactions of highly functional TBI drivers in response to simulated driving obstacles in comparison to matched controls. The present study sought to explore the deficits at the tactical and operational levels of Michon’s hierarchy of driver behavior which may contribute to the increased crash rate among highly functional TBIs as previously observed. Seventeen TBI and 16 control participants completed a simulated drive in which four separate surprising road obstacles were presented. Longitudinal acceleration, longitudinal velocity, lateral velocity and lane position were compared between groups. Results indicated that TBIs were slower to respond, swerved less, and were more cautious immediately after the obstacle had passed. TBI drivers also drove slower overall. Findings support deficits at the tactical and operational levels of Michon’s hierarchy of driver behavior. Differences at the strategic level are also discussed.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.940
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.056
GPT teacher head0.313
Teacher spread0.257 · 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
Published2008
Admission routes1
Has abstractyes

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