A healthy dose of scepticism: Four good reasons to think again about protective effects of alcohol on coronary heart disease
Bibliographic record
Abstract
ISSUES: Alcohol has been implicated in both the popular press and scientific literature as having a protective effect for at least a dozen conditions including coronary heart disease (CHD). APPROACH: Epidemiological evidence for an apparent protective effect of alcohol on CHD is now being challenged on a number of fronts. This paper is a synopsis of those various challenges as they currently stand. KEY FINDINGS: The argument that systematic misclassification of ex-drinkers and occasional drinkers to 'abstainer' categories among epidemiological studies might explain apparent protective effects of moderate alcohol consumption on CHD has recently been supported by new meta-analyses and independent research. The influence of uncontrolled or unknown factors on the relationship between alcohol and disease cannot be ruled out. Exclusion of participants on the basis of ill-health severely reduces study sample size and new analyses suggest that doing so might artificially create the appearance of protective effects. The ability of respondents to accurately recall their own alcohol consumption is in serious doubt and very few individuals maintain one single drinking level or style throughout life. The relationship between alcohol and some conditions might be a function of drinking patterns but few studies have addressed the issue. IMPLICATIONS: Popular perceptions regarding the strength of evidence for alcohol's protective effect on a growing number of conditions might be misguided. CONCLUSION: It is time for the wider research, health and medical community to seriously reflect on the quality of current evidence for apparent protective effects of alcohol on human disease.
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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 | 0.085 | 0.175 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.010 | 0.024 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.018 | 0.048 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".