Impact of health behavior change on health utility (HU) and financial toxicity in head and neck cancer (HNC) survivors.
Bibliographic record
Abstract
11561 Background: Health behavior changes including tobacco cessation and increasing physical activity (PA) are important aspects of cancer survivorship. Understanding how these behaviours impact on HU and financial toxicity will help when evaluating survivorship programs. We evaluated the impact of tobacco cessation and PA on HU, function and financial toxicity among HNC patients (pts). Methods: HNC pts from Princess Margaret Cancer Centre completed questionnaires at baseline (diagnosis) and 12 months between 2014-2018 evaluating tobacco use, PA with the Godin questionnaire, cancer related monthly out of pocket costs (OOPC), HU using HU Index Mark 3, function using Lawton Brody Scale (LBS) and lost annual income. Multivariable linear regression analyses evaluated the impact of health behaviour change on OOPC, HU, LBS and lost income. Results: Among 296 pts, mean age 61, 76% male; 29% smoked at diagnosis, 60% quit 1 year after; 26% met PA guidelines at diagnosis, 52% continued to meet guidelines at 1 year. 19% of those not meeting PA guidelines at diagnosis, met them at 1 year. Among all, mean HU [SEM] was 0.84 [0.01] (baseline), 0.80 [0.01] (12 months); mean monthly OOPC [SEM] were $171 [27] (12 months); mean annual lost individual income was $25897 [2945]. Among smokers at diagnosis, those continuing to smoke at 1 year lost a mean of $21272 (95% CI [$2783-39761] P= 0.03) more in individual annual income compared to pts who quit, adjusted for baseline income and education. Current smokers who quit smoking at 1 year had an adjusted mean increase in HU of 0.15 ([0.00-0.30] P= 0.05) greater than pts continuing to smoke. Pts who continued meeting PA guidelines at 1 year had an adjusted mean increase in HU scores of 0.11 ([0.02-0.20], P= 0.02) compared to those reducing PA levels after diagnosis. Changes in PA and tobacco were not associated with change in function or OOPC; improving to meet PA guidelines after diagnosis was not associated with HU or lost income ( P> 0.05). Conclusions: Quitting smoking and maintaining PA levels after diagnosis were associated with improvements in HU scores; quitting smoking reduced lost income. Cancer survivors should be made aware of the potential economic impact of behaviour change.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".