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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".