Evaluation of a cancer exercise program: patient and physician beliefs
Bibliographic record
Abstract
OBJECTIVE: Participation in an exercise intervention during cancer treatment diminishes the side effects associated with cancer therapies, although such benefits vary according to the disease and the patient characteristics. A structured exercise program providing an individualized fitness program tailored to the patients' illness, treatment, and fitness level would address this variability. However, the need, desired components, and anticipated barriers of such a program have not been systematically explored from either the point of view of cancer patients or treating oncologists. METHODS: Sixty-six cancer patients and 18 medical and radiation oncologists were surveyed on the above variables. RESULTS: Cancer patients and oncologists alike perceived a need for a structured exercise program during and after medical treatment for cancer. Among cancer patients, the most commonly preferred feature was access to consultation with an exercise specialist who could take into account the patient's previous exercise and medical history. Over a third of patients reported interest in a hospital-based fitness program. Oncologists were in favor of appropriate supervision of patients during exercise, and noted insufficient time to discuss exercise in their practice. Respondents noted time and parking as barriers to participation. CONCLUSION: Overall, results support the need for a supervised exercise program during active treatment for cancer and highlight the desired features of such a program.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".