First North American validation and head‐to‐head comparison of four preoperative nomograms for prediction of lymph node invasion before radical prostatectomy
Bibliographic record
Abstract
OBJECTIVES: To perform a head-to-head comparison of four nomograms; namely, the Cagiannos, the 2012-Briganti, the Godoy and the online-Memorial Sloan Kettering Cancer Center (MSKCC), for prediction of lymph node invasion (LNI) in a North American population. PATIENTS AND METHODS: A total of 19 775 patients with clinically localized prostate cancer (PCa) who had undergone radical prostatectomy and pelvic lymph node dissection (PLND) were identified within the Surveillance Epidemiology and End Results (SEER) database. All four nomograms were tested using Heagerty's concordance index (C-index), calibration plots and decision curve analysis (DCA). In addition, we examined specific nomogram-derived thresholds to compare the number of avoided PLNDs and missed LNI-positive cases. RESULTS: All nomograms were found to have highly comparable C-index values: the Cagiannos, 78.6%; the Godoy, 78.2%; the 2012-Briganti, 79.8%; and the MSKCC, 79.9%. The Cagiannos nomogram showed the best calibration, followed by the 2012-Briganti, the Godoy and the online-MSKCC. In DCA, the 2012-Briganti and the Cagiannos, in that order, provided the best results, followed by the Godoy and the online-MSKCC models. For each nomogram, the threshold associated with ≤10% missed LNI cases avoided 8 693 (46.6%), 8 652 (46.4%), 8 461 (45.4%) and 8 590 (46.1%) PLNDs, respectively, with the use of the Cagiannos (2.6% threshold), the online-MSKCC (4.3% threshold), the Godoy (3.6% threshold) and the 2012-Briganti (4.6% threshold) nomograms. CONCLUSION: The Cagiannos and the 2012-Briganti nomograms exhibited the best calibrations and DCA results. Conversely, C-index values and ability to avoid unnecessary PLNDs were virtually the same for all four nomograms examined.
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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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".