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Record W2398417420

Motor Vehicle Injury Valuation for Canada’s National Collision Database Economic Analyses

2016· article· en· W2398417420 on OpenAlexaboutno aff
Ted R. Miller, Nancy Bergeron, Bruce A. Lawrence

Bibliographic record

VenueTransportation Research Board 95th Annual MeetingTransportation Research Board · 2016
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsnot available
Fundersnot available
KeywordsCollisionValuation (finance)Motor vehicle crashNational databasePoison controlInjury preventionActuarial scienceDatabaseForensic engineeringEconomicsEngineeringComputer scienceComputer securityMedicineFinanceMedical emergency
DOInot available

Abstract

fetched live from OpenAlex

The objective of this study is to estimate fractions of the Value of Statistical Life (VSL) to value motor vehicle collision injuries in Transport Canada Motor Vehicle Safety Directorate’s economic analyses. Canada’s police-reported National Collision Database (NCDB) definitions for minimal, minor and major injuries are used in conjunction with US 2010 trauma data in order to estimate quality-adjusted life year (QALY) losses, which are then translated into fractions of VSL given an expected remaining lifespan. Findings are compared to other estimates in the literature and are found to be largely in line with them. They are therefore deemed valid for use in economic analyses of Canadian motor vehicle collisions using NCDB injury data.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.094
GPT teacher head0.397
Teacher spread0.304 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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