An external validation of the nocera nomogram: Predicting non-organ confined stage of ≥pT3 in cT1 clear cell renal cell carcinoma
Bibliographic record
Abstract
Background: Only one previously published study by Nocera et al. addressed the risk of upstaging to ≥pT3 in cT1 clear cell renal cell carcinoma (ccRCC) by using characteristics of the R.E.N.A.L and PADUA score (age, tumor size, rim location, exophytic rate, polar involvement) developing an accurate nomogram. However, this nomogram has never been externally validated yet. Material and methods: The study cohort consisted of 288 patients with cT1a-b ccRCC, diagnosed between 2008-2021 at the University Hospital Frankfurt, Germany. Analyses addressed clinical, tumor and radiographic characteristics. The external validation of the nomogram relied on accuracy calculations derived from the area under the curve of the receiver operator characteristic analysis. Results: . 13.0%), and medial rim location significantly differed between the pT1-2 and ≥pT3 group (all p ≤ 0.01). In subgroup analyses of small renal mass ccRCC patients (<4cm, cT1a), only 3.8% (n=6) patients had ≥pT3 pathology. Upstaged patients were significantly older and more frequently had endophytic tumor than pT1-2 counterparts (p<0.05). The external validation of the Nocera nomogram showed a good accuracy of 76.6%. Using the suggested cut-off of 21%, 26.5% of patients exhibited ≥pT3 ccRCC. Conversely, within patients below cut-off, 5.9% patients exhibited ≥pT3 ccRCC. Conclusion: We reported the first external validation of the nomogram addressing the risk of ≥pT3 in cT1 ccRCC patients, demonstrating a good accuracy, with a low false-negative rate. Therefore, the nomogram can accurately be used for patients' counselling and treatment decision making.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".