Safety and effectiveness of everolimus compared with sirolimus and tacrolimus in preventing kidney transplantation rejection: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Everolimus is an immunosuppressive agent with a novel mode of action but has a different clinical role with calcineurin inhibitors (CNI). The aim of this study was to evaluate the safety and effectiveness of everolimus compared with sirolimus and tacrolimus in preventing kidney transplantation rejection. Methods: Search was conducted for finding randomized clinical trials (RCT) until the end of 2013 in main databases include Cochrane, Medline and other related databases in February 2014. To find the ongoing trials two databases were searched (Clinicaltrial.gov and irct.ir/fa/). Two independent reviewers checked studies for quality and eligibility and finally extracted the data. Data extraction was performed using Cochrane data extraction form for clinical trial. Discrepancies were resolved via consultation with third person. The studies examined in term of heterogeneity with I2 and Chi-square test. The mata-analysis was carried out using RevMan 5.2 (Wintertree Software Inc, Ontario, Canada) when there was homogeneity. Results: Finally, seven reports from six RCTs included in this study. All reports were in english language and total numbers of participant in these studies were 824. No studies were found in comparison of everolimus and sirolimus and all seven reports were combination of everolimus, tacrolimus and other relative drugs. Follow up time of studies were different from 6 to 36 months. Due to the heterogeneity of included studies, only two studies were entered into meta-analysis. The recorded mean values for glomerular filtration rate, serum creatinine and creatinine clearance were between 60-80 ml/min, 50 to 80 ml/min and 1.2 to 1.9 mg/dl respectively. The results of meta-analysis in three outcomes include; serum creatinine, creatinine clearance and glomerular filtration rate were significantly in favor of low dose tacrolimus plus everolimus. Conclusion: In general, everolimus showed better results in combination with tacrolimus. Given the available evidence in this study, everolimus in combination with low dose tacrolimus showed better safety and effectiveness in preventing kidney transplantation rejection.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.051 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".