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 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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".