Validation of the Meet-URO score in patients with metastatic renal cell carcinoma receiving first-line nivolumab and ipilimumab in the Italian Expanded Access Program
Bibliographic record
Abstract
BACKGROUND: The Meet-URO score allowed a more accurate prognostication than the International Metastatic RCC Database Consortium (IMDC) for patients with pre-treated metastatic renal cell carcinoma (mRCC) by adding the pre-treatment neutrophil-to-lymphocyte ratio and presence of bone metastases. MATERIALS AND METHODS: A post hoc analysis was carried out to validate the Meet-URO score on the overall survival (OS) of patients with IMDC intermediate-poor-risk mRCC treated with first-line nivolumab plus ipilimumab within the prospective Italian Expanded Access Programme (EAP). We additionally considered progression-free survival (PFS) and disease response rates. Harrell's c-index was calculated to compare the accuracy of survival prediction. RESULTS: Overall the EAP included 306 patients, with a median follow-up of 12.2 months, median OS was not reached, 1-year OS was 66.8% and median PFS was 7.9 months. By univariable analysis, both the IMDC score and the two additional variables of the Meet-URO score were associated with either OS or PFS (P < 0.001 for all comparisons). The four Meet-URO risk groups (G) had 1-year OS of 92%, 72%, 50% and 21% for G2 (29.1% of patients), G3 (28.8%), G4 (33.0%) and G5 (9.1%), respectively. OS was significantly shorter in each consecutive G (P = 0.001 for G3, P < 0.001 for both G4 and G5 compared to G2). Similarly, Meet-URO Gs 2-5 showed decreasing median PFS and response rates. The Meet-URO score showed the highest c-index for both OS (0.73) and PFS (0.67). Limitations include the post hoc nature of this analysis and the lack of a comparative arm to assess predictive value. CONCLUSION: The Meet-URO score appeared to show better prognostic classification than the IMDC alone in patients with mRCC at IMDC intermediate-poor risk treated with first-line nivolumab and ipilimumab.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".