Change in International mRCC Database Consortium (IMDC) prognostic category and implications for efficacy of second-line targeted therapy.
Bibliographic record
Abstract
534 Background: Currently no predictive markers exist for choosing second-line targeted therapy (2L) in metastatic renal cell carcinoma (mRCC). A change in IMDC prognostic group when calculated at first-line therapy (1L) and 2L and its association with 2L efficacy was examined. Methods: The IMDC database was interrogated for patients who received 1L VEGF inhibitors (VEGFi) and then 2L with VEGFi or an mTOR inhibitor (mTORi). IMDC prognostic categories (Favorable, F; Intermediate, I; Poor, P) were defined prior to each line of therapy. Overall survival (OS), time to treatment failure (TTF) and response to 1L or 2L were assessed in relation to change in IMDC prognostic risk category. Results: Data for 1516 patients were analyzed; 89% had clear cell histology. Prognostic risk categories at 1L were F: 21.7%; I: 59.5%; P: 18.8%. 60.3% of patients remained in the same risk category at start of 2L; 9.0% improved (3% I→F; 6% P→I); 30.7% deteriorated (14% F → I or P; 16% I → P). Improvement in prognostic risk category was associated with better response and longer duration of 1L. Patients who improved prognostic risk (I → F or P → I), or maintained I or F grouping, had longer TTF if they remained on VEGFi for 2L compared to those who switched to mTORi (p < 0.05). In contrast, patients whose risk category deteriorated (F → I or P) may be more likely to benefit from switching to mTORi. Conclusions: Changes in IMDC prognostic category may predict the subsequent clinical course of patients with aRCC and provide a rational basis for selection of subsequent therapy. [Table: see text]
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".