A population‐based study evaluating the impact of sunitinib on overall survival in the treatment of patients with metastatic renal cell cancer
Bibliographic record
Abstract
BACKGROUND: Sunitinib has replaced interferon (IFN) as a first-line standard of care in the treatment of metastatic renal cell carcinoma (RCC). This study aimed to determine overall survival and to confirm effectiveness in a population that includes poor prognosis patients. METHODS: Data were collected on all patients identified by the BC Cancer Registry with metastatic RCC who were treated with IFN or sunitinib. The IFN group consisted of patients who received IFN between January 2000 and October 2005, and the sunitinib group included patients treated with first-line sunitinib from October 2005 to September 2007. RESULTS: There were 131 and 69 patients in the IFN and sunitinib groups, respectively. The median follow-up of those still alive was 12.6 months. The median age (62 vs 63 years; P = .41), Memorial Sloan Kettering Cancer Center (MSKCC) prognostic criteria (poor in 19% vs 30%; P = .41), and proportion with >1 metastasis (53% vs 62%; P = .21) were similar between the IFN and sunitinib groups, respectively. The median survival of the IFN and sunitinib groups was 8.7 and 17.3 months, respectively (log-rank P = .004). The median survival of patients with favorable, intermediate, and poor MSKCC prognostic profiles in the IFN group was 22.9, 8.7, and 4.1 months, respectively (P < .001), whereas in the sunitinib group it was not reached, 16.8, and 10.7 months, respectively (P = .006). The hazard ratio of death after adjusting for MSKCC criteria was 0.49 (95% confidence interval, 0.31-0.76; P = .001). CONCLUSIONS: The introduction of first-line sunitinib was associated with a doubling of overall survival compared with patients treated with IFN alone. This benefit extended to patients with poor MSKCC prognostic profiles.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".