Effects of Exercise Intervention on Persons with Metastatic Cancer: A Systematic Review
Bibliographic record
Abstract
PURPOSE: To systematically review and synthesize results of studies that examined the effects of exercise on persons with metastatic cancer. METHODS: Databases searched were MEDLINE, EMBASE, CINAHL, PsycINFO, and Cochrane Central Register of Controlled Trials. Search terms used were "metastatic or advanced or palliative AND cancer or neoplasms AND exercise or physical activity or exercise therapy or physical fitness." The search identified 8 studies published in 10 articles between 2000 and 2007. Two quality-assessment tools were used, and levels of evidence were assigned according to the Oxford Centre for Evidence-Based Medicine (CEBM) guidelines. RESULTS: Of the eight studies, three were randomized controlled trials (RCTs) and five were case series. Because three of the RCTs were based on the same sample, only one was included in the results and discussion sections. CEBM levels of evidence ranged from 2b to 4. Two overall recommendations were made, one grade A and one grade C. Exercise interventions ranged from aerobic exercise and strength training to multidimensional programmes with exercise as one component. All studies used physical and/or quality-of-life outcome measures. CONCLUSIONS: Review findings suggest that exercise may be beneficial for persons with metastatic cancer. Future research should clarify optimal exercise dose parameters.
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".