Prognostic nomogram to predict progression-free survival in patients with platinum-sensitive recurrent ovarian cancer
Bibliographic record
Abstract
BACKGROUND: Patients with platinum-sensitive recurrent ovarian cancer are a heterogeneous group, and it is not possible to accurately predict the progression-free survival (PFS) in these patients. We developed and validated a nomogram to help improve prediction of PFS in patients treated with platinum-based chemotherapy. METHODS: The nomogram was developed in a training cohort (n=955) from the CALYPSO trial and validated in the AGO-OVAR 2.5 Study (n=340). The proportional-hazards model (nomogram) was based on pre-treatment characteristics. RESULTS: The nomogram had a concordance index (C-index) of 0.645. Significant predictors were tumour size platinum-chemotherapy-free interval, CA-125, number of organ metastatic sites and white blood count. When the nomogram was applied without CA-125 (CA-125 was not available in validation cohort), the C-indices were 0.624 (training) and 0.594 (validation). When classification was based only on the platinum-chemotherapy-free interval, the indices were 0.571 (training) and 0.560 (validation). The calibration plot in the validation cohort based on four predictors (without CA-125) suggested good agreement between actual and nomogram-predicted 12-month PFS probabilities. CONCLUSION: This nomogram, using five pre-treatment characteristics, improves prediction of PFS in patients with platinum-sensitive ovarian cancer having platinum-based chemotherapy. It will be useful for the design and stratification of patients in clinical trials and also for counselling patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".