Comparing cancer survivors' perceptions on lifestyle behaviors between adolescent-young adult (AYA) and middle-aged patients (pts).
Bibliographic record
Abstract
207 Background: Lifestyle behaviours such as smoking, physical activity, and alcohol consumption are important determinants of cancer survivorship. Previous studies have compared the lifestyle behaviours of elderly and middle-aged patients (pts), yet no studies have compared these behavioural perceptions between AYA (aged 18-39 years) to those of middle-aged pts (MA, 40-64 years). Methods: Cancer pts across various tumour types at a comprehensive cancer centre were surveyed with respect to their perceptions of how their well-being was affected by smoking, physical activity and alcohol consumption after diagnosis. Univariate logistic regression models evaluated factors associated with perceptions on the effect of various adverse lifestyle behaviours on health and well-being. Results: Of 200 AYA (57% female, 43% male) and 772 MA (56% female, 44% male) pts, a positive smoking history was reported by 33% of AYA and 48% of MA (P<0.001). At time of diagnosis, 55% of AYA and 59% of MA pts consumed alcohol, 16% of AYA and 16% of MA were ex-drinkers, and 28% of AYA and 25% of MA were never drinkers (P=0.62). Among AYA, 26% exercised compared to 20% in the MA group (P=0.19). The majority (72-92%) of pts perceived that smoking and lack of activity after cancer diagnosis negatively affected quality of life, survival chances, and fatigue; there were no significant differences between age groups. In contrast, both age cohorts displayed misperceptions about how alcohol affects health, which was characterized by perceiving neutral or beneficial influence on their overall well-being: Fifty-seven percent of MA pts had a borderline greater misperception versus 49% of AYA pts (P=0.06). Misperceptions regarding how alcohol affects survival were observed in 49% of AYA pts and 58% of MA pts (P=0.05). Misperception with respect to how alcohol affects fatigue was observed in 40% of AYA pts compared to 52% of MA pts (P=0.005). Furthermore, MA pts had 1.63 (95% CI 1.16–2.29) times the odds to have misperceptions regarding how alcohol affects fatigue, and 1.41 (95% CI 1.01–1.97) times the odds to have misperceptions on how alcohol affects survival compared to AYA pts. Conclusions: Both the AYA and MA population were not adequately informed about how alcohol affects cancer survivorship health; with more misperceptions in MA pts. Results from this study advocate for survivorship programs to implement emphasis on the deleterious effects of alcohol, with particular efforts tailored to the MA group.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".