Multiple lines of chemotherapy for patients with high‐grade ovarian cancer: Predictors for response and effect on survival
Bibliographic record
Abstract
Guidelines for the treatment of tubo-ovarian cancer patients beyond third line are lacking. We aimed to evaluate the effect of response in each line on patient's outcome as well as identify variables that predict response for additional line of chemotherapy. A cohort study was performed including all patients with advanced high-grade ovarian cancer. Survival analysis was performed using Kaplan-Meier curves and log-rank tests. Odds ratios and hazard ratios were calculated using multilevel, mixed-effects logistic regression and Cox regression, adjusting for repeated measures within individual patients. Two-hundred thirty-eight patients were included and underwent up to 10 lines of chemotherapy. The median progression-free survival was 15.6 and overall survival (OS) was 55.6 months. Response rates dropped with each additional line and by line 5, most patients (61%) became refractory and only 16% had any type of response (complete 4% or partial 12%). By line 2, whether a patient had partial disease (PR), stable disease (SD) or progressive disease (PD) did not have an effect on the OS. From line 2, whether a patient had PR, SD or PD did not have an effect on chemotherapy-free interval. Number of previous lines and time from previous line were the only variables that significantly correlated with both outcome of patients and response to the next line. In conclusion, time interval from the previous line of chemotherapy is the major clinical factor that predicts beneficial effect of another line of treatment in patients with ovarian cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".