Alcohol drinking and multiple myeloma risk – a systematic review and meta-analysis of the dose–risk relationship
Bibliographic record
Abstract
The role of alcohol intake in the risk for multiple myeloma (MM) is unclear, although some recent findings suggest an inverse relationship. To summarize the information on the topic, we carried out a systematic review and a dose-risk meta-analysis of published data. Through the literature search until August 2013, we identified 18 studies, eight case-control and 10 cohort studies, carried out in a total of 5694 MM patients. We derived pooled meta-analytic estimates using random-effects models, taking into account the correlation between estimates, and we carried out a dose-risk analysis using a class of nonlinear random-effects meta-regression models. The relative risk for alcohol drinkers versus non/occasional drinkers was 0.97 [95% confidence interval (CI), 0.85-1.10] overall, 0.96 (95% CI, 0.74-1.24) among case-control studies, and 1.00 (95% CI, 0.89-1.13) among cohort studies. Compared with nondrinkers, the pooled relative risks were 0.96 (95% CI, 0.81-1.13) for light (i.e. ≤ 1 drink/day) and 0.89 (95% CI, 0.74-1.07) for moderate-to-heavy (i.e. >1 drink/day) alcohol drinkers. The dose-risk analysis revealed a model-based MM risk reduction of about 15% at two to four drinks/day (i.e. 25-50 g of ethanol). The present meta-analysis of published data found no strong association between alcohol drinking and MM risk, although a modest favorable effect emerged for moderate-to-heavy alcohol drinkers.
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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 | 0.012 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".