Sorafenib tolerability in elderly patients with advanced renal cell carcinoma: results from a large pooled analysis
Bibliographic record
Abstract
BACKGROUND: Elderly patients tend to be underrepresented in renal cell carcinoma (RCC) clinical trials. The Sorafenib RCC Integrated Database includes data from six clinical trials and two expanded-access studies evaluating sorafenib monotherapy in >4600 patients with RCC. Using this database, sorafenib tolerability and treatment patterns were analysed according to age group (<55, 55-<65, 65-<75, or ≥ 75 years). METHODS: Dosing patterns, and incidence, prevalence and cumulative incidence of drug-related adverse events (DRAEs) and fatal DRAEs were assessed. RESULTS: Overall, 4684 patients were evaluable (<55 years, n=1126; 55-<65, n=1579; 65-<75, n=1382; ≥ 75, n=559). Treatment patterns were generally similar across subgroups, although sorafenib treatment duration was ∼30% shorter in the ≥ 75-years subgroup. There were no substantial differences in any-grade DRAEs with sorafenib between subgroups. Drug-related adverse events and dose modifications due to DRAEs tended to occur in months 0-3 and declined thereafter; there was no evidence of cumulative toxicity. Fatal DRAEs were rare (0.7% overall; 95% confidence interval, 0.5-1.0%). CONCLUSION: Sorafenib was well tolerated regardless of age in a heterogeneous population of RCC patients.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".