Adjuvant Low-Dose Interleukin-2 (IL-2) Plus Interferon-α (IFN-α) in Operable Renal Cell Carcinoma (RCC)
Bibliographic record
Abstract
There is currently no standard therapy to reduce the recurrence rate after surgery for renal cell carcinoma (RCC). The aim of this study was to assess efficacy and safety of adjuvant treatment with low doses of interleukin-2 (IL-2)+interferon-α (IFN-α) in operable RCC. The patients were randomized 1:1 to receive a 4-week cycle of low-dose IL-2+IFN-α or observation after primary surgery for RCC. Treatment cycles were repeated every 4 months for the first 2 years and every 6 months for the subsequent 3 years. The primary endpoint was recurrence-free survival (RFS); safety; and overall survival (OS) were secondary endpoints. ClinicalTrials.gov registration number was NCT00502034. 303/310 randomized patients (156 in the immunotherapy arm and 154 in the observation group) were evaluable at the intention-to-treat analyses. The 2 arms were well balanced. At a median follow-up of 52 months (range, 12-151 mo), RFS, and OS were similar, with an estimated hazard ratio (HR) of 0.84 [95% confidence interval (CI), 0.54-1.31; P=0.44] and of 1.07 (95% CI, 0.64-1.79; P=0.79), respectively in the 2 groups. Unplanned, subgroup analysis showed a positive effect of the treatment for patients with age 60 years and younger, pN0, tumor grades 1-2, and pT3a stage. Among patients with the combined presence of ≥ 2 of these factors, immunotherapy had a positive effect on RFS (HR=0.44; 95% CI, 0.24-0.82; P ≤ 0.01), whereas patients with <2 factors in the treatment arm exhibited a significant poorer OS (HR=2.27; 95% CI, 1.03-5.03 P=0.037). Toxicity of immunotherapy was mild and limited to World Health Organization grade 1-2 in most cases. Adjuvant immunotherapy with IL-2+IFN-α showed no RFS or OS improvement in RCC patients who underwent radical surgery. The results of subset analysis here presented are only hypothesis generating.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".