Sarcopenia as a predictor of chemotherapy toxicity in patients with ovarian cancer receiving platinum and taxane-based chemotherapy.
Bibliographic record
Abstract
e17035 Background: Sarcopenia (age-related loss of skeletal muscle mass) is associated with worse oncologic outcomes and adverse events in patients with solid tumors. Limited research in epithelial ovarian cancer (EOC) has shown that sarcopenia is associated with worse patient outcomes. The purpose of this study was to investigate the association of sarcopenia with chemotherapy toxicity in patients with EOC. Methods: EOC patients diagnosed between 6/2000 and 2/2017 who received platinum and taxane-based chemotherapy were included. Age, race, stage, grade, BMI, comorbidities, treatment, and outcomes were collected. Chemotherapy toxicities and associated grades, as well as treatment modifications were recorded. Computed tomography (CT) images within 3 months of diagnosis were evaluated for Skeletal Muscle Area (SMA) at the 3rd lumbar vertebrae. Measurements were obtained with use of TomoVision(R) radiological software (SliceOmatic – version 5.0, Quebec, Canada). Sarcopenia was defined as Skeletal Muscle Index (SMI = SMA/height2) ≤ 41. This preliminary analysis generated descriptive statistics and compared the distributions of toxicity assessments by sarcopenia, with t-tests, rank-sum, chi-square and fisher’s exact tests. Results: 144 patients were evaluated. The majority presented with Stage III /IV, HGSOC (75%, 73%). Median age was 63 yrs and median BMI was 27.2 (IQR = 23.7-32.8). 61% of patients were sarcopenic. The median SMI was 40 (IQR = 34.2-46.2). There was no significant difference between sarcopenic (S) and nonsarcopenic (NS) patients with respect to dose adjustment (p = 0.37), regimen change (p = 0.998), need for blood transfusion (p = 0.60), toxicity-related hospitalization (p = 0.84) or delay in treatment (p = 0.37). Grade 3-4 toxicity was found in 56.8% of S and 58.9% of NS patients (p = 0.992). Similarly, the distributions of grade 1-2 toxicities in S and NS patients were comparable. Conclusions: In this cohort of patients, there was no significant association between sarcopenia and chemotherapy related toxicities. However, given high prevalence of sarcopenia in ovarian cancer patients, our relatively small sample size, and challenges with retrospective collection of chemotherapy toxicity, further prospective exploration is warranted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".