Risk of infections in renal cell carcinoma (RCC) and non-RCC patients treated with mammalian target of rapamycin inhibitors
Bibliographic record
Abstract
BACKGROUND: Mammalian target of rapamycin (mTOR) inhibitors are used in a variety of malignancies. Infections have been reported with these drugs. We performed an up-to-date meta-analysis to further characterise the risk of infections in cancer patients treated with these agents. METHODS: Pubmed and oncology conferences' proceedings were searched for studies from January 1966 to June 2012. Studies were limited to phase II and III randomised controlled trials (RCTs) of everolimus or temsirolimus reporting on cancer patients with adequate safety profiles. Summary incidences, relative risks (RRs), and 95% confidence intervals (CIs) were calculated. RESULTS: A total of 3180 patients were included. The incidence of all-grade and high-grade infections due to mTOR inhibitors was 33.1% (95% CI, 24.5-43.0%) and 5.6% (95% CI, 3.8-8.3%), respectively. Compared with controls, the RR of all-grade and high-grade infections due to mTOR inhibitors was 2.00 (95% CI, 1.76-2.28, P<0.001) and 2.60 (95% CI, 1.54-4.41, P<0.001), respectively. Subgroup analysis found no difference in incidences or risks between everolimus and temsirolimus or between different tumour types (renal cell carcinoma (RCC) vs non-RCC). Infections included respiratory tract (61.7%), genitourinary (29.4%), skin/soft tissue (4.2%), and others (4.9%). CONCLUSION: Treatment with mTOR inhibitors is associated with a significant increase in risk of infections. Close monitoring for any signs of infections is warranted.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.018 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".