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Record W2136590028 · doi:10.1016/s0386-1112(14)60134-5

THE RISK OF INJURY AND VEHICLE DAMAGE SEVERITY IN VEHICLE MISMATCHED SIDE IMPACT CRASHES IN BRITISH COLUMBIA

2005· article· en· W2136590028 on OpenAlexaffabout
Ediriweera Desapriya, Ian Pike

Bibliographic record

VenueIATSS Research · 2005
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsSpinal Cord Injury BCUniversity of British Columbia
Fundersnot available
KeywordsTruckCrashPoison controlVehicle safetyInjury preventionOccupational safety and healthHarmCrashworthinessEngineeringTransport engineeringAutomotive engineeringAeronauticsEnvironmental healthMedicineComputer sciencePolitical scienceLaw

Abstract

fetched live from OpenAlex

As occupant protection offered by new passenger vehicles has improved, there has been growing concern about the harm that some vehicle designs may inflict on occupants of other vehicles with which they collide. Preceding analyses of crash statistics have clearly demonstrated the incompatibility between passenger sedan cars (PS) and pick-up trucks (PU) involved in side impact crashes in British Columbia. A comparison of light truck and passenger car crashes in previous literature reveals that light truck vehicles inflict greater harm than passenger cars for a number of reasons including their greater weight, stiffer structure, and higher ride height. These features place occupants of passenger cars at a disadvantage should they be involved in a collision with a light truck vehicle. The injury risk for passenger sedan car occupants is greater than the risk for pick-up truck occupants in two-vehicle crashes (Odds Ratio (OR) 1.87; 95% Confidence Interval (CI) 1.38-2.52). In addition, the risk of vehicle damage severity was increased for passenger cars compared with pick-up trucks (write off vehicle-OR 5.35; 95% CI 3.75-7.63, severely damaged vehicles-OR 5.87; 95% CI 4.79–7.19, moderately damaged vehicles-OR 2.86; 95% CI 2.44–3.36). There is strong justification for injury prevention experts and policy makers to step up motor vehicle crash injury prevention advocacy by implementing evidence-based policies to reduce rates of injury as a result of passenger sedan cars and pick-up trucks involved in side impact crashes in the province of British Columbia.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.178
Threshold uncertainty score0.951

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.029
GPT teacher head0.363
Teacher spread0.333 · 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

Citations11
Published2005
Admission routes2
Has abstractyes

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