Body mass index (BMI), health behaviors, and perceptions in cancer survivors.
Bibliographic record
Abstract
97 Background: Obesity is associated with poorer outcomes across multiple cancer types. Health behaviour change (smoking cessation, physical activity (PA) and alcohol moderation) can improve both obesity and outcomes among cancer survivors. Methods: Cancer patients (pts) were cross-sectionally surveyed on their smoking, PA and alcohol use before and after diagnosis and their perceptions of these behaviours on quality of life (QoL), fatigue, survival (OS) and safety. Multivariable logistic regression models evaluated the association of BMI 1 year prior to diagnosis with behaviour changes and perceptions. Results: Of 1269 pts, 204 smoked at diagnosis and 58% quit afterwards; 350 met PA guidelines at diagnosis and 238 at follow-up; 661 drank alcohol at diagnosis and 50% reduced consumption afterwards. Median BMI was 25.8 (22% obese). Most pts perceived PA ( > 75%) as improving outcomes, smoking ( > 70%) as worsening outcomes and half (41-49%) felt alcohol worsened outcomes. At diagnosis, increased BMI was associated with being an ex-smoker (vs current smoker; P= 0.003), never using alcohol (vs former use; P= 0.05) and not meeting PA guidelines ( P= 0.01). Among smokers at diagnosis, increased BMI was associated with smoking cessation (aOR 1.08 per 1 unit BMI increase, P= 0.03) and perceiving that smoking worsens OS (aOR 1.10, P= 0.04) and fatigue (aOR 1.08, P= 0.08). Among pts not meeting PA guidelines at diagnosis, increased BMI was associated with perceiving that PA worsens fatigue (OR 1.02, P= 0.06) and is unsafe (OR 1.04, P= 0.06). Among drinkers at diagnosis, increasing BMI was associated with perceiving alcohol to be less harmful (aOR 0.93, P= 0.002), less likely to worsen OS (aOR 0.96, P= 0.04) and fatigue (aOR 0.97, P= 0.09). BMI was not associated with changes in alcohol or PA after diagnosis. BMI was not associated with counselling rates; however, 66% of current smokers received cessation counselling while only 14% of current drinkers and 13% of pts not meeting PA guidelines received counselling on their respective behaviours. Conclusions: Obese pts were more likely to quit smoking and perceive it to be harmful but less likely to perceive alcohol as harmful. Survivorship programs should consider focusing on PA and alcohol counselling in obese pts.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".