Estimating alcohol‐attributable fractions for injuries based on data from emergency department and observational studies: a comparison of two methods
Bibliographic record
Abstract
AIM: To compare the injury alcohol-attributable fractions (AAFs) estimated using emergency department (ED) data to AAFs estimated by combining population alcohol consumption data with corresponding relative risks (RRs). DESIGN: Comparative risk assessment. SETTING AND PARTICIPANTS: ED studies in 27 countries (n = 24 971). MEASUREMENTS: AAFs were estimated by means of an acute method using data on injury cases from 36 ED studies combined with odds ratios obtained from ED case-cross-over studies. Corresponding AAFs for injuries were estimated by combining population-level data on alcohol consumption obtained from the Global Information System on Alcohol and Health, with corresponding RRs obtained from a previous meta-analysis. FINDINGS: ED-based injury AAF estimates ranged from 5% (Canada 2002 and the Czech Republic) to 40% (South Africa), with a mean AAF among all studies of 15.4% (18.9% for males and 8.4% for females). Population-based injury AAF estimates ranged from 21% (India) to 51% (Spain and the Czech Republic), with a mean AAF among all country-years of 36.8% (42.5% for males and 22.5% for females). The Pearson correlation coefficient for the two types of injury AAF estimates was 0.09 for the total, 0.06 for males and 0.32 for females. CONCLUSIONS: Two methods of estimating the injury alcohol-attributable fractions-emergency department data versus population method-produce widely differing results. Across 36 country-years, the mean AAF using the population method was 36.8%, more than twice as large as emergency department data-based acute estimates, which average 15.4%.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.252 | 0.491 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.024 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".