Sarcopenia as a Determinant of Chemotherapy Toxicity and Time to Tumor Progression in Metastatic Breast Cancer Patients Receiving Capecitabine Treatment
Bibliographic record
Abstract
PURPOSE: Body composition has emerged as an important prognostic factor in cancer patients. Severe depletion of skeletal muscle (sarcopenia) and, hence, of overall lean body mass may represent an occult condition in individuals with normal or even high body weight. Sarcopenia has been associated with poor performance status, 5-fluorouracil toxicity, and shortened survival in cancer patients. Here, we prospectively studied patients with metastatic breast cancer receiving capecitabine treatment in order to determine if sarcopenia was associated with a higher incidence of toxicity and a shorter time to tumor progression (TTP). EXPERIMENTAL DESIGN: Fifty-five women with metastatic breast cancer resistant to anthracycline and/or taxane treatment were included. Skeletal muscle cross-sectional area at the third lumbar vertebra was measured by computerized tomography, and sarcopenia was defined using a previously published cutoff point. Toxicity was assessed after cycle 1 of treatment, and TTP was determined prospectively. RESULTS: Approximately 25% of patients were classified as sarcopenic, and this feature was seen in normal weight, overweight, and obese individuals. Toxicity was present in 50% of sarcopenic patients, compared with only 20% of nonsarcopenic patients (P = 0.03), and TTP was shorter in sarcopenic patients (101.4 days; confidence interval, 59.8-142.9) versus nonsarcopenic patients (173.3 days; confidence interval, 126.1-220.5; P = 0.05). CONCLUSION: Sarcopenia is a significant predictor of toxicity and TTP in metastatic breast cancer patients treated with capecitabine. Our results raise the potential use of body composition assessment to predict toxicity and individualize chemotherapy dosing.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".