Sarcopenia and serum biomarkers of oxidative stress after a 6-month physical activity intervention in women with metastatic breast cancer: results from the ABLE feasibility trial
Bibliographic record
Abstract
PURPOSE: Sarcopenia has been identified as an important prognostic factor for patients with cancer. This study aimed at exploring the potential associations between a 6-month physical activity intervention and muscle characteristics, sarcopenia, oxidative stress and toxicities in patients with metastatic breast cancer. METHODS: ) and muscle quality (poor if muscle attenuation < 37.8 Hounsfield Units). Oxidative markers included plasma antioxidant enzymes (catalase, glutathione peroxidase and superoxide dismutase activities), prooxidant enzymes (NADPH oxidase and myeloperoxidase activities) and oxidative stress damage markers (advanced oxidation protein products, malondialdehyde (MDA) and DNA oxidation. RESULTS: At baseline 53% (mean age 55 years (SD 10.41)) were sarcopenic and 75% had poor muscle quality. Muscle cross sectional area, skeletal muscle radiodensity, lean body mass remained constant over the six months (p = 0.75, p = 0.07 and p = 0.75 respectively), but differed significantly between sarcopenic and non-sarcopenic patients at baseline and 6-months. Sarcopenic patients at baseline were more likely to have an increase of MDA (p = 0.02) at 6 months. Being sarcopenic during at least one moment during the 6-month study was associated with a higher risk of developing severe toxicities (grade > 2) (p = 0.02). CONCLUSIONS: This study suggests potential benefits of physical activity for maintenance of muscle mass. Sarcopenia can alter many parameters and disturb the pro and antioxidant balance.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".