Alcohol and Cancer: Existing Knowledge and Evidence Gaps across the Cancer Continuum
Bibliographic record
Abstract
Alcoholic beverages are carcinogenic to humans. Globally, an estimated 4.1% of new cancer cases in 2020 were attributable to alcoholic beverages. However, the full cancer burden due to alcohol is uncertain because for many cancer (sub)types, associations remain inconclusive. Additionally, associations of consumption with therapeutic response, disease progression, and long-term cancer outcomes are not fully understood, public awareness of the alcohol-cancer link is low, and the interrelationships of alcohol control regulations and cancer risk are unclear. In December 2020, the U.S. NCI convened a workshop and public webinar that brought together a panel of scientific experts to review what is known about and identify knowledge gaps regarding alcohol and cancer. Examples of gaps identified include: (i) associations of alcohol consumption patterns across the life course with cancer risk; (ii) alcohol's systemic carcinogenic effects; (iii) alcohol's influence on treatment efficacy, patient-reported outcomes, and long-term prognosis; (iv) communication strategies to increase awareness of the alcohol-cancer link; and (v) the impact of alcohol control policies to reduce consumption on cancer incidence and mortality. Interdisciplinary research and implementation efforts are needed to increase relevant knowledge, and to develop effective interventions focused on improving awareness, and reducing harmful consumption to decrease the alcohol-related cancer burden.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".