Impact of the Discordance Between Scales of Memorial Sloan-Kettering Cancer Center and International Metastatic Renal Cell Carcinoma Database Consortium in Patients’ Prognosis With Metastatic Renal Cancer
Bibliographic record
Abstract
Background: In Mexico, about 30% of renal cancer patients are diagnosed in a metastatic state. Despite the recent advances in the treatment of cancer, metastatic renal cancer is still an incurable illness. Thus, identifying prognostic factors helps improve prognosis accuracy and survival prediction for patients. Methods: In this study, we retrospectively analyzed 26 patients with histological diagnosis of renal cell carcinoma, including clear cell and other subtypes in stage IV (metastatic), recurrent or unresectable disease. We performed a multivariate analysis of overall survival regarding the congruity between prognostic scales. Results: Our results showed a significant difference in favor of patients with congruity between scales for progression-free survival (18.9 vs. 3.1 months; P = 0.048) and a tendency towards better overall survival in patients with the congruity of both scales compared to the discordant patients (112 vs. 32 months; P = 0.99). Conclusion: This study highlights the discordance between Memorial Sloan-Kettering Cancer Center and International Metastatic Renal Cell Carcinoma Database Consortium scales, which was associated with worse prognosis with a significant difference in progression-free survival but not in overall survival.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".