Biases inherent in all-cause mortality studies: implications for shaping the 2025–2030 dietary guidelines for Americans on alcohol consumption
Bibliographic record
Abstract
Alcohol is an addictive substance that has both detrimental and protective health outcomes at low doses.1 The net impact of alcohol on health at low doses depends on the underlying risks of diseases and injuries causally linked to alcohol and thus varies across countries. As such, informing the public about the health risks of alcohol consumption requires country-specific information. The Review of Evidence on Alcohol and Health by the National Academies of Sciences, Engineering, and Medicine (NASEM) aims to inform the 2025–2030 Dietary Guidelines for Americans.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: yes · About a Canadian topic: yes | Theoretical or conceptual | low |
| grok | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| opus | Metaresearch Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | medium |
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 | 0.671 | 0.799 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.012 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".