The International Scientific Forum on Alcohol Research (ISFAR) critiques of alcohol research: Promoting health benefits and downplaying harms
Bibliographic record
Abstract
Abstract Background and Aims The International Scientific Forum on Alcohol Research (ISFAR), many of whose members are linked to the alcohol industry, has published over 280 critiques on alcohol and health research. This study investigated whether ISFAR critiques favour studies reporting alcohol's health benefits while being more critical of those identifying harms. We also examined whether industry‐funded studies are more likely to report benefits, and whether ISFAR's critiques reflect the methodological rigor of the studies they assess. Methods We analysed 268 ISFAR critiques published between April 2010 and January 2024, manually coding each underlying study for its content (whether the original study reported alcohol‐related health benefits or harms) and each critique for its tone (positive or negative). Sentiment analysis (SA) algorithms were applied to critique summaries to assess tone using automated methods. Study authors were examined for prior receipt of alcohol industry funding. AMSTAR‐2 and ROBIS tools evaluated risk of bias in 36 systematic reviews and meta‐analyses favoured ( n = 24) or criticised ( n = 12) by ISFAR. Results Studies reporting health benefits had higher odds of receiving positive reviews from ISFAR [odds ratio (OR) = 6.50, 95% confidence interval (95% CI) = (3.62–12.00)], as did studies minimising alcohol harms [OR = 2.47, 95% CI = (1.40–4.45)]. Studies reporting health harms had higher odds of receiving negative critiques [OR = 0.29, 95% CI = (0.15–0.14)], as did studies minimising health benefits [OR = 0.21, 95% CI = (0.10–0.41)]. Algorithmic SA replicated these patterns, though the correlation with manual coding was modest [ r = 0.20, 95% CI = (0.08–0.32)]. Studies with industry ties had higher odds of minimising alcohol‐related harms [OR = 1.90, 95% CI = (1.04–3.50)], and those co‐authored by ISFAR members had higher odds of reporting a J‐shaped relationship between alcohol use and health [OR = 2.52, 95% CI = (1.00–6.48)]. No association was found between ISFAR sentiment and study quality as independently assessed by AMSTAR‐2 and ROBIS (BF 01 = 6.13–6.21). Conclusion Critiques from The International Scientific Forum on Alcohol Research (ISFAR) consistently promote alcohol's purported health benefits while minimising evidence of harm, regardless of study quality. These patterns provide a valuable resource for industry actors to shape public perception, downplay risk and influence policy—using strategies that closely resemble those historically employed by the tobacco industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".