Exercise Prescription for People With Metastatic Cancer of the Skeleton
Bibliographic record
Abstract
ABSTRACT Exercise is increasingly accepted as a therapy in the management of cancer, and is now described as a medicine, giving rise to a new discipline in clinical practice and research termed exercise oncology. Exercise medicine has been evaluated in clinical trials and implemented in patient care at all phases of disease and treatment trajectory. Advanced disease involving bone metastases presents considerable challenges in terms of patient assessment and exercise prescription. Over the past decade research evidence has accumulated attesting to the safety and efficacy of appropriately designed exercise medicine interventions. Combined with a need for well-developed guidelines, an expert consensus has been developed. Through a rigorous process the overarching recommendation was that exercise professionals should work with the patient and their health care team to balance the risk of adverse events due to participation in exercise therapy against the risk of more rapid patient decline through not exercising, as well as the potential loss of health benefits that could be realized through exercise. This is the basic tenet of health care and withholding or not offering a therapy that is likely to provide greater benefit than the potential risk it may cause for fear of that risk is untenable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".