Effect of exercise interventions on hospital length of stay and admissions during cancer treatment: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of participating in an exercise intervention compared with no exercise during cancer treatment on the duration and frequency of hospital admissions. DESIGN: Systematic review and meta-analysis. DATA SOURCES: MEDLINE, EMBASE, PEDro and Cochrane Central Registry of Randomized Controlled Trials. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Randomised studies published until August 2023 evaluating exercise interventions during chemotherapy, radiotherapy or stem cell transplant regimens, compared with usual care, and which assessed hospital admissions (length of stay and/or frequency of admissions). STUDY APPRAISAL AND SYNTHESIS: Study quality was assessed using the Cochrane Risk-of-Bias tool and Grading of Recommendations Assessment, Development and Evaluation assessment. Meta-analyses were conducted by pooling the data using random-effects models. RESULTS: Of 3918 screened abstracts, 20 studies met inclusion criteria, including 2635 participants (1383 intervention and 1252 control). Twelve studies were conducted during haematopoietic stem cell transplantation regimens. There was a small effect size in a pooled analysis that found exercise during treatment reduced hospital length of stay by 1.40 days (95% CI: -2.26 to -0.54 days; low-quality evidence) and lowered the rate of hospital admission by 8% (difference in proportions=-0.08, 95% CI: -0.13 to -0.03, low-quality evidence) compared with usual care. CONCLUSION: Exercise during cancer treatment can decrease hospital length of stay and admissions, although a small effect size and high heterogeneity limits the certainty. While exercise is factored into some multidisciplinary care plans, it could be included as standard practice for patients as cancer care pathways evolve.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.050 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".