Physical activity and patient-reported outcomes: enhancing impact
Bibliographic record
Abstract
Abstract Physical activity (PA) is beneficial for cancer survivors across the cancer trajectory. Evidence indicates physical and psychosocial benefits, and ultimately, enhanced overall quality of life, for individuals who are more versus less active (Semin Oncol Nurs 23:285–296, 2007; Cancer Epidemiol Biomarkers Prev 14:1672–1680, 2005; J Cancer Surviv 4:87–100, 2010). A number of recent reviews have been conducted that examine different patient or survivor populations and outcomes. In general, the findings across the reviews reveal potential positive associations between exercise (structured activity one engages in for the purposes of enhancing health-related fitness outcomes) and PA (any physical movement, including lifestyle types of activity) with both physical and psychological outcomes. It is important to note, however, that depending on the nature of the review and the types of studies included in the review, the strength of the findings (i.e., effect size) vary. Despite this overwhelmingly positive evidence for the benefits of PA, activity levels are very low among cancer survivors, with one study reporting only 22 % of survivors as active enough to achieve health benefits (Cancer 112(11):2475–2482, 2008). This suggests that we must begin to better understand the factors that impact the uptake and maintenance of PA among cancer survivors. These potential factors are important when considering the patient-reported outcomes to assess and can include timing (i.e., during or after treatment completion), characteristics of the cancer diagnosis and subsequent treatments (i.e., early vs. late stage cancers), and characteristics of the individual (i.e., older vs. younger).
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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 | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".