New Sarculator Prognostic Nomograms for Patients With Primary Retroperitoneal Sarcoma
Bibliographic record
Abstract
OBJECTIVE: To update the current Sarculator retroperitoneal sarcoma (RPS) prognostic nomograms considering the improvement in patient prognosis and the case volume effect. BACKGROUND: Survival of patients with primary RPS has been increasing over time, and the volume-outcome relationship has been well recognized. Nevertheless, the specific impact on prognostic nomograms is unknown. METHODS: All consecutive adult patients with primary localized RPS treated at 8 European and North American sarcoma reference centers between 2010 and 2017 were included. Patients were divided into 2 groups: high-volume centers (HVC, ≥13 cases/year) and low-volume centers (LVC, <13 cases/year). Primary end points were overall survival (OS) and disease-free survival (DFS). Multivariable analyses for OS and DFS were performed. The nomograms were updated by recalibration. Nomograms performance was assessed in terms of discrimination (Harrell C index) and calibration (calibration plot). RESULTS: The HVC and LVC groups comprised 857 and 244 patients, respectively. The median annual primary RPS case volume (interquartile range) was 24.0 in HVC (15.0-41.3) and 9.0 in LVC (1.8-10.3). Five-year OS was 71.4% (95% CI: 68.3%-74.7%) in the HVC cohort and 63.3% (56.8%-70.5%) in the LVC cohort ( P =0.012). Case volume was associated with both OS (LVC vs. HVC hazard ratio 1.40, 95% CI: 1.08-1.82, P =0.011) and DFS (hazard ratio 1.93, 95% CI: 1.57-2.37, P <0.001) at multivariable analyses. When applied to the study cohorts, the Sarculator nomograms showed good discrimination (Harrell C index between 0.68 and 0.73). The recalibrated nomograms showed good calibration in the HVC group, whereas the original nomograms showed good calibration in the LVC group. CONCLUSIONS: New nomograms for patients with primary RPS treated with surgery at high-volume versus low-volume sarcoma reference centers are available in the Sarculator app.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.002 | 0.001 |
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".