Impact of physical activity on the prevention of breast cancer in postmenopausal women: A Protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Breast cancer is responsible for the highest rates of incidence and mortality among neoplasms in women. There is a lack of evidence regarding the different domains and intensities of physical activity (PA) in relation to the risk of breast cancer occurrence in menopause. The objective will be to evaluate the impact of different domains and intensities of PA in preventing breast cancer in postmenopausal women. Databases, including MEDLINE/PubMed, Scopus, Web of Science, and Embase, will be used for the search. A search strategy was developed to retrieve observational studies (case-control and cohort) that have evaluated occurrence of breast cancer and practice of PA in postmenopausal women. No date or language restrictions will be applied. Two authors will independently select the studies meeting the inclusion criteria by screening the title, abstract, and full text. Data will be extracted, and the risk of bias will be evaluated using the Newcastle-Ottawa scale (NOS). Review Manager 5.4.1 and software R version 4.3.1 will be used for data synthesis. The Grading of Recommendation Assessment, Development, and Evaluation will be used to assess the strength of the evidence. This systematic review and meta-analysis will summarize the comparative efficacy of types, domains and intensities of PA in reducing the risk of breast cancer in postmenopausal women.
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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 | 0.069 | 0.104 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.053 | 0.005 |
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".