External validation of a multi‐institutional retroperitoneal sarcoma nomogram
Bibliographic record
Abstract
BACKGROUND: A multi-institutional nomogram for predicting disease-free survival (DFS) and overall survival (OS) in patients with primary retroperitoneal sarcoma (RPS) incorporating relevant prognostic factors not included in the American Joint Committee on Cancer staging system for soft tissue sarcoma has been reported. The authors validated this nomogram with an independent, transatlantic cohort. METHODS: Data from patients with RPS who were undergoing definitive resection at 1 of 6 sarcoma centers in Europe and North America ("validation set") were used to validate a RPS nomogram developed from 3 other centers ("development set"). The nomogram incorporated 6 variables: age, tumor size, grade, histologic subtype, multifocality, and quality of surgery. Nomogram-predicted probabilities were stratified into 6 subgroups and compared with observed outcomes. Discriminative ability was quantified by Harrell C statistics. RESULTS: The validation and development sets included 631 and 523 patients, respectively, all of whom underwent surgical resection at the institutions represented. The 7-year DFS and OS rates for the validation set were 38% (95% confidence interval, 34%-43%) and 58% (95% confidence interval, 53%-63%), respectively. All 6 nomogram variables were found to be independently prognostic. The corrected Harrell C statistics concordance index values for the validation set were 0.69 for DFS and 0.73 for OS, which were similar to those for the development set, suggesting good calibration of the nomogram in the validation cohort. CONCLUSIONS: The RPS nomogram was externally validated using a larger, independent cohort. The nomogram can be generalized to patients undergoing surgery for RPS by specialized sarcoma surgeons at sarcoma centers. The nomogram provides a more individualized and disease-relevant estimation of OS compared with the American Joint Committee on Cancer classification. Cancer 2016;122:1417-1424. © 2016 American Cancer Society.
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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 | 0.023 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".