Relationship between Handgrip Strength and Muscle Mass in Female Survivors of Breast Cancer: A Mediation Analysis
Bibliographic record
Abstract
This study explored the mediating factors of sarcopenia in a group of women survivors of breast cancer in Bogotá, Colombia. This was a descriptive cross-sectional study with 98 women survivors of breast cancer, who were registered with the SIMMON (Integrated Synergies to Improve Oncological Management in Colombia) Foundation. Body weight, height, and waist circumference (WC) were measured, and body mass index (BMI) was calculated. Body composition (percentage of fat and muscle mass) was evaluated via four-pole bioelectrical impedance analysis. Sarcopenia was defined as low muscle mass plus low grip strength or low gait speed (European Working Group on Sarcopenia in Older People (EWGSOP) criteria). A “causal” mediation analysis with the Baron & Kenny procedure (PROCESS® macro, Columbus, OH, USA) was used to explore variables related to sarcopenia. Analyses were performed with the IBM SPSS 21 statistical package (SPSS Inc., Chicago, IL, USA). The significance level of the results obtained in the hypothesis contrast was p < 0.05. The mean age of the sample was 65.5 ± 5.9 years, with a BMI of 27.8 ± 4.7 kg/m2. The prevalence of sarcopenia was 22.4%. Linear regression models suggest a partial mediation of anthropometric parameters (body mass, body mass index and waist circumference) in the association between handgrip strength and muscle mass. In conclusion, one in every five women survivors of breast cancer had sarcopenia. The findings seem to emphasize the importance of obesity prevention in women survivors of breast cancer, suggesting that high handgrip strength may not relate closely to greater muscle mass and therefore would not exclude the risk of sarcopenia.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".