Patterns, perceptions and their association with changes in alcohol consumption in cancer survivors
Bibliographic record
Abstract
Continued consumption of alcohol after a cancer diagnosis is associated with poorer outcomes. We evaluated whether perceptions of the effects of continued alcohol use and receiving information on moderating alcohol reduced alcohol consumption in adult cancer survivors. A total of 509 cancer survivors were cross-sectionally surveyed at follow-up for their alcohol use before and after cancer diagnosis and perceptions of continued drinking. Multivariable logistic regression models evaluated factors associated with changes in alcohol consumption after diagnosis. Among 299 patients who were drinking alcohol at diagnosis (13% exceeding gender-specific guidelines), 52% reduced/ceased alcohol consumption 1 year after diagnosis. Patients perceiving that alcohol worsened their own (a) quality of life, (b) cancer-related fatigue or (c) overall survival were more likely (aORs = 2.43-3.35, p < 0.002) to reduce (moderating or quitting) their alcohol use 1 year after diagnosis. Only 14% of individuals currently drinking regularly recalled receiving information/counselling from healthcare providers on alcohol consumption (7% from oncologists). However, there was a significant fourfold to sixfold increase in cessation with such information/counselling (p < 0.01). Similar trends were observed in patients exceeding gender-specific guidelines. Perception of negative effects of alcohol use on their health by cancer survivors was associated with reducing harmful alcohol consumption. Counselling, especially from the oncologist, may play a significant role for reducing consumption.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".