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Record W2770968500 · doi:10.1503/cmaj.170337

Direct medical costs of motorcycle crashes in Ontario

2017· article· en· W2770968500 on OpenAlexafffundvenueabout
Daniel Pincus, David Wasserstein, Avery B. Nathens, Yu Bai, Donald A. Redelmeier, Walter P. Wodchis

Bibliographic record

VenueCanadian Medical Association Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsCentre for Disability Prevention and RehabilitationHealth Sciences CentreUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
FundersOntario Ministry of Health and Long-Term CareInstitute for Clinical Evaluative Sciences
KeywordsComputer sciencePoison controlInjury preventionHuman factors and ergonomicsOccupational safety and healthSuicide preventionData scienceMedical emergencyComputer securityMedicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: There is no reliable estimate of costs incurred by motorcycle crashes. Our objective was to calculate the direct costs of all publicly funded medical care provided to individuals after motorcycle crashes compared with automobile crashes. METHODS: We conducted a population-based, matched cohort study of adults in Ontario who presented to hospital because of a motorcycle or automobile crash from 2007 through 2013. For each case, we identified 1 control absent a motor vehicle crash during the study period. Direct costs for each case and control were estimated in 2013 Canadian dollars from the payer perspective using methodology that links health care use to individuals over time. We calculated costs attributable to motorcycle and automobile crashes within 2 years using a difference-in-differences approach. RESULTS: < 0.001). INTERPRETATION: Considering both the attributable cost and higher rate of injury, we found that each registered motorcycle in Ontario costs the public health care system 6 times the amount of each registered automobile. Medical costs may provide an additional incentive to improve motorcycle safety.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.810
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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.

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

Citations29
Published2017
Admission routes4
Has abstractyes

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