The association between body composition and toxicities from the combination of Doxil and trabectedin in patients with advanced relapsed ovarian cancer
Bibliographic record
Abstract
Emerging research suggests that body composition can predict toxicity of certain chemotherapeutic agents. We used data from a clinical study to investigate associations between body composition and combined DOXIL (pegylated liposomal doxorubicin; PLD) and trabectedin (Yondelis) treatment, an effective treatment for ovarian cancer that shows high interpatient variation in toxicity profile. Patients (n = 74) participating in a phase III randomized trial of relapsed advanced ovarian cancer receiving PLD (30 mg/m(2)) and trabectedin (1.1 mg/m(2)) were included. Muscle tissue was measured by analysis of computerized tomography images, and an extrapolation of muscle and adipose tissue to lean body mass (LBM) and fat mass (FM) were employed. Toxicity profile after cycle 1 was used and graded according to the National Cancer Institute Common Toxicity Criteria (version 3). Patients presented with a wide range of body composition. In overweight and obese patients (body mass index (BMI) ≥ 25 kg/m(2), n = 48) toxicity was more prevalent in those with lower BMI (p = 0.028) and a lower FM (n = 43, p = 0.034). Although LBM alone was not predictive of toxicity, a lower FM/LBM ratio was the most powerful variable associated with toxicity (p = 0.006). A different pattern emerged among normal weight patients (n = 26) where toxicity was rare among patients with smaller BMI (<21 kg/m(2)). A clear association between both FM and LBM (primarily driven by FM) in explaining PLD plus trabectedin toxicity emerged, but only in individuals with excess body weight, with a lower ratio predicting higher exposure and risk for toxicity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".