Association of Handgrip Strength with Quality of Life in Breast Cancer Survivors: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Handgrip strength (HGS) is an indicator of general muscular strength and in cancer patients acts as a relevant marker associated with mortality and health. This study aimed to evaluate the association between peripheral muscle function and health-related quality of life (HRQoL) in breast cancer (BC) survivors. METHODS: Systematic review registered on PROSPERO under number: CRD 42021225206. The searches were carried out on MEDLINE via Pubmed, PEDro, Cochrane Library, Embase, CINAHL via EBSCO and Science Direct databases. Observational studies evaluating the association between handgrip strength (HGS) and HRQoL in adult female BC survivors were included. No linguistic or time restrictions were applied. Two reviewers reviewed full texts for inclusion and performed data extraction and risk of bias using the Newcastle and Ottawa scale (NOS). RESULTS: Five articles were included and involved 587 patients, mean age of 47 to 59 years. The percentage of decreased HGS ranged from 38.3% to 60.3%. HGS was associated with different quality of life measures. From meta-analysis including 220 patients, the correlation coefficient between HGS and HRQoL was 0.26 (95% CI: 0.07-0.35). CONCLUSIONS: Breast cancer survivors face decline of HGS. In this population HGS was correlated with HRQoL. However, more evidence are necessary.
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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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".