Interferon-alfa in the treatment of patients with inoperable locally advanced or metastatic renal cell carcinoma: a systematic review
Bibliographic record
Abstract
A systematic review was undertaken to determine whether interferon-alfa (IFN-alpha) is an effective treatment for patients with inoperable locally advanced or metastatic renal cell carcinoma (mRCC). MEDLINE, EMBASE, the Cochrane Library, guideline databases and relevant meeting proceedings were searched. Randomized clinical trials (RCTs) or meta-analyses comparing IFN-alpha-containing regimens to placebo or non-immunotherapy controls, and that reported response rate, survival, toxicity or quality of life data were eligible. Two systematic reviews and eight RCTs met the selection criteria. A Cochrane review updated in 2005 reported higher response rates and reduced one-year mortality based on 4 RCTs in patients who received IFN-alpha. Of the eight RCTs, three reporting objective response rate showed significant differences favouring IFN-alpha. Two of five trials reporting survival data showed longer median survival in the IFN-alpha group. Adverse effects of IFN-alpha were consistent across the trials with increased intensity and frequency concordant with increased IFN-alpha dose. Meta-analyses of seven RCTs for objective response and six RCTs for mortality favoured IFN-alpha: odds ratio 6.87 (95% Confidence Interval [CI], 3.29 to 14.35) and hazard ratio 0.79 (95% CI, 0.69 to 0.91), respectively. The effectiveness of IFN-alpha in mRCC has been subject to skepticism. As IFN-alpha has been used as a control arm in RCTs of new targeted therapies, therapies which not all patients may have access to, information about its effectiveness remains relevant. These data confirm genuine, if modest, effectiveness of IFN-alpha in mRCC.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".