Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Small Cell Carcinoma of Ovary Patients: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: This study aimed to develop functional nomograms to predict overall survival (OS) and cancer-specific survival (CSS) of small cell carcinoma of ovary (SCCO). Methods: SSCO case data were recruited retrospectively from the Surveillance, Epidemiology, and End Results (SEER) database. Nomograms were constructed to predict the probabilities of OS and CSS in SCCO patients based on independent predictors. The predictive performance of nomogram was evaluated with the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Results: The independent risk factors affecting the prognosis of SCCO patients were older age, lower income, surgery, chemotherapy, radiation, advanced International Federation of Gynecology and Obstetrics (FIGO) stage, and number of primary tumors. The C-index for the OS nomogram was 0.78 (95% confidence interval (CI): 0.75 - 0.82), and AUCs for 1-, 3-, and 5-year OS were 0.861, 0.807, and 0.821, respectively. The C-index for the CSS nomogram was 0.79 (95% CI: 0.76 - 0.83), and AUCs for 1-, 3-, and 5-year OS were 0.873, 0.841, and 0.864, respectively. The calibration curves indicated reasonable agreement between the observed and predicted probabilities of the OS and CSS nomograms, which indicated a good degree of confidence. According to the C-index, ROC, and DCA, the prognostic nomograms of OS and CSS showed better prediction accuracy and clinical application value for SCCO than the FIGO staging system. Conclusions: We constructed original nomograms that provided useful prediction of OS and CSS for patients with SCCO. These models could facilitate the postoperative personalized assessment and the identification of treatment strategy.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".