IUC24358-87 Prognostic assessment of the Meet-URO score compared with the IMDC score in metastatic renal cell carcinoma (mRCC) receiving first-line systemic therapies (Meet-URO 33 study)
Bibliographic record
Abstract
Abstract Background The prognostic stratification is the cornerstone of treatment decision-making for mRCC. The novel Meet-URO score (IMDC score + NLR + Bone metastases) was developed in the immunotherapy era and has shown better prognostic performance compared with the IMDC score in different settings. Its application in the first-line IO-TKI setting was awaited. Methods The Meet-URO 33 is a multicentric prospective observational study enrolling mRCC patients receiving first-line systemic therapy. A retrospective cohort of patients treated from 01.01.2021 was included. The Meet-URO score was assessed compared with the IMDC score in predicting OS. An exploratory analysis on PFS was also conducted. Results A total of 1,557 patients were enrolled, 1400 (90%) were assessable. Median age was 66 years, 75% were males, 84% had clear cells, and 64% underwent nephrectomy; 20% received IO-IO, 66% IO-TKI (32% Pembrolizumab+Axitinib) and 14% TKI; 45% had NLR ≥ 3.2 and 29% bone metastases. After a mFU of 14.1 months, mOS was 40.5 months, and mPFS was 16.8 months. The Meet-URO score confirmed a better prognostic stratification compared with the IMDC score (c-index 0.714 vs 0.688) (Table 1). Although the Meet-URO score was developed as an OS model, it showed a similar PFS performance (c-index 0.62 vs 0.61). Conclusions The Meet-URO score confirmed its better prognostic accuracy compared with the IMDC score, also in a large-scale prospective cohort receiving first-line therapy. The adoption of the Meet-URO score should be implemented in clinical practice and as a stratification factor of clinical trials for more individualized patient management.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".