Real world experience of immuno-oncology agents in metastatic renal cell carcinoma: Results from the IMDC.
Bibliographic record
Abstract
492 Background: Immuno-oncology (IO) checkpoint inhibitors have demonstrated efficacy in metastatic renal cell cancer (mRCC) treatment. Real world data is required to assess outcomes when applied to the general population. Methods: A retrospective analysis was performed using the IMDC database. It included mRCC patients treated with IO agents, including atezolizumab (Atezo), avelumab, ipilimumab, nivolumab (Nivo), and pembrolizumab (Pembro). Some patients were treated with combination therapy with a targeted agent. Patients may have received IO therapy as first-, second-, third-, or fourth-line treatment. Overall survival (OS), treatment duration, and overall response rates (ORR) were calculated. Results: 255 patients with mRCC treated with IO therapy were included. The ORR to IO therapy in those patients who were evaluable was 29% (32% first-, 22% second-, 33% third-, and 32% fourth-line therapy). Patients treated with second-line IO therapy were divided into favorable, intermediate, and poor risk using IMDC criteria; the corresponding median OS rates were not reached, 26.7 mo, and 12.1 mo, respectively (p<0.0001). Conclusions: Response rates to IO therapies appear to remain consistent no matter which line of therapy it is used in. Within second-line treatment, IMDC criteria appear to stratify patients appropriately into favorable, intermediate, and poor risk groups. Survival data are premature and will be updated. In contrast to Nivo clinical trial data, where median treatment duration was 5.5 mo, longer treatment length is observed in real world practice. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".