Characteristics of long-term and short-term survivors of metastatic renal cell carcinoma (mRCC) treated with targeted therapy: Results from the International mRCC Database Consortium.
Bibliographic record
Abstract
4538 Background: Patients with mRCC have variable courses in terms of survival and response to targeted therapy. The patients at the two extremes of the survival spectrum need to be characterized. Methods: 2,161 patients with mRCC treated with targeted therapy were examined. 152 patients who survived 4 years or more after the initiation of targeted therapy (long-term) were compared with 218 patients who survived 6 months or less (short-term) over the same time period (2004-2007). Results: Long-term survivors had fewer poor prognostic factors (PFs) such as Karnofsky performance status (KPS) <80%, diagnosis to treatment interval<1 yr, hypercalcemia, anemia, thrombocytosis and neutrophilia (all p<0.0001). Patients with favorable prognosis who responded to targeted therapy were more likely to be long term survivors. For those in the intermediate risk group, patients who were long-term survivors were more likely to have only 1 poor prognostic factor (73% vs. 28%, p<0.0001) and KPS≥80% (88% vs. 69%, p=0.009) compared to those in the short term survivor group. On multivariable analysis adjusting for PFs, response to targeted therapy (PR or better) significantly predicted long term survivor status (odds ratio=6.3, 95% CI: 2.3,17.4, p=0.0004). Conclusions: Long term survivors had a higher response rate to targeted therapy, a longer treatment duration and more use of second-line targeted therapy. Baseline prognostic criteria may be able to discriminate between long- and short- term survivors. [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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".