Checkpoint inhibitors in metastatic renal cell carcinoma patients including elderly subgroups: Results from the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC).
Bibliographic record
Abstract
4580 Background: Immuno-oncology (IO) checkpoint inhibitor treatment outcomes are poorly characterized in the real world metastatic renal cell cancer (mRCC) patient population, including geriatric patients. Methods: Using the IMDC database, a retrospective analysis was performed on mRCC patients treated with IO, as listed below. Patients received one or more lines of IO therapy, with or without a targeted agent. Duration of treatment (DOT) and overall response rates (ORR) were calculated. Cox regression analysis was performed to examine the association between age as a continuous variable and DOT. Results: 312 mRCC patients treated with IO were included. In patients who were evaluable, ORR to IO therapy was 29% (32% first-, 22% second-, 33% third-, and 32% fourth-line treatment (Tx)). Patients treated with second-line IO therapy were divided into favorable, intermediate, and poor risk using IMDC criteria; the corresponding median DOT rates were not reached (NR), 8.6 mo, and 1.9 mo, respectively (p<0.0001). Based upon age, hazard ratios were calculated in the first- through fourth-line therapy setting, ranging from 1.03 to 0.97. Conclusions: The ORR to IO appears to remain consistent, regardless of line of therapy. In the second-line, IMDC criteria appear to appropriately stratify patients into favorable, intermediate, and poor risk groups for DOT. Premature OS data will be updated. In contrast to clinical trial data, longer DOT is observed in real world practice. Age may not be a factor influencing DOT. [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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".