Recreational physical activity reduces breast cancer recurrence in female survivors of breast cancer: A meta-analysis
Bibliographic record
Abstract
PURPOSE: The aim of this review was to systematically collect the published evidence to assess the effect of recreational physical activity (PA) in reducing breast cancer (BC) recurrence in female survivors. METHODS: A bibliographic search was carried out in PubMed Medline, CINAHL Complete, Scopus and Lilacs until June 2021. We selected observational studies or clinical trials comprising women diagnosed with BC, in which the risk of recurrence of BC was measured before or after performing recreational PA. As a secondary outcome, we analyzed disease free survival for recurrence/disease-specific mortality. The methodological quality of observational studies was assessed with the Newcastle-Ottawa Scale and clinical trials with the PEDro scale. A random effects model was used to estimate the relative risks (RR) and their 95% confidence intervals (CI) to infer the results for any female survivor of BC. We performed separate meta-analyses for prediagnosis and postdiagnosis recreational PA. RESULTS: Eleven studies were included, providing data from 29,677 surviving women with BC with an age range of 18-79 years old. Postdiagnosis recreational PA reduced the risk of BC recurrence by 16% (RR, 0.84; 95% CI: 0.78 to 0.91) and the risk of recurrence/disease-specific mortality by 23% (RR, 0.77; 95% CI: 0.66 to 0.93). Prediagnosis PA reduced the risk of BC by 18% (RR, 0.82; 95% CI: 0.74 to 0.91). CONCLUSION: This systematic review with meta-analysis shows that recreational PA can be an interesting therapeutic tool to protect against recurrence of BC in female survivors.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".