Re-Evaluating Chemotherapy Dosing Strategies for Ovarian Cancer: Impact of Sarcopenia
Bibliographic record
Abstract
We investigated the impact of sarcopenia on adjuvant chemotherapy dosing in advanced epithelial ovarian cancer (EOC). The chemotherapy dosing and toxicity of 173 eligible patients who underwent cytoreductive surgery and adjuvant chemotherapy at a single institution were analyzed. Patients with a skeletal muscle index less than 39 cm2/m2 measured on a CT scan were considered sarcopenic. Sarcopenic and non-sarcopenic patients were compared with regard to relative dose intensity (RDI), completion of scheduled chemotherapy, toxicity, and survival. A total of 62 (35.8%) women were sarcopenic. Sarcopenic women were less likely to complete at least six cycles of chemotherapy (83.9% vs. 95.5%, p = 0.02). The mean RDI for both carboplatin (80.4% vs. 89.4%, p = 0.03) and paclitaxel (91.9% vs. 104.1%, p = 0.03) was lower in sarcopenic patients compared to non-sarcopenic patients. Despite these differences in chemotherapy, there was no difference in neutropenia or median overall survival (3.99 vs. 4.57 years, p = 0.62) between the sarcopenic and non-sarcopenic women, respectively. This study highlights the importance of considering lean body mass instead of body weight or surface area in chemotherapy dosing formulas for sarcopenic women with advanced EOC. Further research is needed to optimize chemotherapy strategies based on individual body composition, potentially leading to improved dosing strategies in this population.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".