Perceptions of benefits and harms of alcohol consumption as predictors of alcohol cessation in adult cancer survivors.
Bibliographic record
Abstract
9595 Background: Survivorship programs are being developed at many cancer centers, addressing secondary prevention and healthy lifestyle issues. We evaluated whether perceptions regarding the harms and benefits of alcohol use influenced alcohol cessation or recidivism in adult cancer survivors. Methods: 531 cancer patients of all subtypes were surveyed at a comprehensive cancer center for their alcohol habits before and after cancer diagnosis and their perception of benefits/harms for continued drinking. Multivariate logistic regression models evaluated the association of each variable with change in alcohol consumption after diagnosis adjusted for significant socio-demographic and clinico-pathological covariates. Results: Among 325 current drinkers at diagnosis, 55% quit or cut down their alcohol consumption 1 year after diagnosis, while 16% of 95 ex-drinkers at diagnosis restarted drinking at 1 year. Negative perceptions of the effects of alcohol on the individual patient were strongly associated with cessation: the adjusted odds ratio (aOR) of quitting were significant for a perceived negative effect on quality of life (aOR=2.2, p=0.006), survival (aOR=3.8, p=1.3E-5), fatigue (aOR=3.1, p=4.6E-5) or an increased chance in self-harm (aOR=2.6, p=0.01). Perceptions of how alcohol affected the average cancer patient had similar associations. While perceptions did not influence alcohol recidivism rates, receiving chemotherapy was the only variable associated with continued abstinence (aOR=5.5, p=0.007). Although only 8% of patients received alcohol cessation information from an oncologist, it had the greatest impact on cessation (aOR=6.6, p=0.006), an association not seen with other information sources or other healthcare providers. Conclusions: Perception to the negative effects of alcohol use on their health in cancer survivors strongly predicted for alcohol cessation. The oncologist had a most significant counselling role for alcohol cessation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".