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Record W2115809269 · doi:10.1093/aje/kwn215

Determination of Lifetime Injury Mortality Risk in Canada in 2002 by Drinking Amount per Occasion and Number of Occasions

2008· article· en· W2115809269 on OpenAlexafffundabout
Brian Taylor, Jürgen Rehm, Robin Room, Jayadeep Patra, Susan J. Bondy

Bibliographic record

VenueAmerican Journal of Epidemiology · 2008
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsCentre for Addiction and Mental Health
FundersUniversity of TorontoTechnische Universität Dresden
KeywordsMedicineEnvironmental healthPoison controlInjury preventionDemographyOccupational safety and healthMedical emergencyEmergency medicinePathology

Abstract

fetched live from OpenAlex

Injury is the leading cause of alcohol-attributable mortality in Canada. Risk is determined by amount consumed per occasion and accumulates across drinking episodes. The authors estimated alcohol-attributable injury mortality in Canada for 2002 by combining the absolute risk of injury unrelated to alcohol with relative risks that were specific to gender and consumption per occasion, while taking into account lifetime number of drinking occasions. The absolute risk increased as number of drinking occasions and number of drinks per occasion increased. The absolute risk remained relatively low at fewer than 2 drinking occasions per month, regardless of number of drinks. Absolute risk levels reached 1 in 1,000 at 5 or more drinks once per month for men and at 5-7 drinks once per month for women. The probability of mortality was 1 in 100 for all levels of consumption above 3 drinks 3 times per week for men and above 5 drinks 3 times per week for women. No safe level of consumption is recommended based on these results, although risk is much lower for consuming 3 standard drinks or less fewer than 3 times per week. Absolute risk reflects long-term effects of drinking patterns and is important for risk-communication and alcohol-control policy.

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 score0.103
Threshold uncertainty score0.911

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.271
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

Citations46
Published2008
Admission routes3
Has abstractyes

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