Efficacy of targeted drug therapy for metastatic renal cell carcinoma in the elderly patient population.
Bibliographic record
Abstract
318 Background: Targeted therapy has become the mainstay of treatment for metastatic renal cell carcinoma (mRCC). The efficacy of this therapy on the older population is poorly understood. Methods: Data from patients with mRCC treated with first-line anti-VEGF therapy were collected through the International mRCC Database Consortium from 14 centers. Results: One thousand three hundred eighty-one patients were treated with targeted therapy as their first-line treatment. Of those, 144 (10%) were seventy-five years or older (median=78 years, range=75–89). Four percent of these individuals were favorable risk, 69% intermediate risk, and 27% poor risk as per Heng et al. JCO 2009 prognostic factors. There was no statistical difference in these prognostic groups between the older (≥75) and younger populations (<75) (p=0.1779). The initial treatment for those ≥ 75 years was with sunitinib (n=98), sorafenib (n=35), bevacizumab (n=7), and AZD2171 (n=4). The older population had fewer nephrectomies (71% vs. 80%, p=0.0133) and fewer brain metastases (3% vs. 9%, p=0.0128). Only 23% of older patients went on to receive second line therapy in comparison to 39% of the younger population (p<0.0001). The overall response rate, median treatment duration and overall survival for the older vs. younger group were 18% vs. 25% (p=0.0975), 5.5 months vs. 7.5 months (p=0.1388), and 16.8 months vs. 19.7 months (p=0.3321), respectively. When adjusted for known poor prognostic factors, age over 75 years was not found to be associated with poorer overall survival (HR 1.002, 95%CI 0.781–1.285) or shorter treatment duration (HR 1.018, 95%CI 0.827–1.252). Conclusions: Overall response rates, treatment duration, and overall survival rates are not different between the older and younger populations and age is not a prognostic factor. Thus, the decision to treat with targeted therapy should not depend on age alone. [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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".