First-line mTOR inhibition in metastatic renal cell carcinoma (mRCC): An updated analysis from the International mRCC Database Consortium (IMDC).
Bibliographic record
Abstract
e15518 Background: FDA approval of the mTOR inhibitors (mTORi) in mRCC was based on efficacy in poor risk patients (pts) in the first line setting for temsirolimus (T) and in VEGF inhibitor-refractory pts for everolimus (E). Little is known about T’s effectiveness in good and intermediate risk patients and E’s outcomes in the first line setting. Methods: We interrogated the IMDC for the outcomes of pts who received mTORi as first-line targeted therapy. Results: 127 pts received a first line mTORi; the majority received T (93 T, 34 E). The main reasons for T administration were poor risk (38%), non-clear cell histology (27%), and clinical trial (15%) whereas clinical trial (82%) and non-cc histology (6%) drove E use. Of the T and E pts, 58% and 32% were poor risk, respectively. Median age was 61 years and median KPS was 80%. 68% had prior nephrectomy (62% T vs. 82% E). Median progression-free survival (PFS) and overall survival (OS) are detailed below. In the 97 pts with response data, 5% and 53% for T and 8% and 58% for E achieved partial responses and stable disease, respectively. Progressive disease as best response occurred in 41% for T and 33% for E. Second line therapy was captured in 52 pts (41%), of whom 48 received VEGF inhibitors. Conclusions: Given the different populations in which they were administered, direct comparisons of the frontline efficacy of T vs. E cannot be made. The majority of T pts were poor risk, which their dismal PFS and OS reflect. The better outcomes in the E pts highlight that the majority were not poor risk and were healthy enough for clinical trials. While limited by small numbers, this data characterizes a real world experience of mTORi in the first line setting. [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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".