Effect of mycophenolate mofetil on blood pressure: a meta-analysis
Bibliographic record
Abstract
Abstract Background: Long-term treatment of immunosuppressive agent have been proved to induce hypertension. The relative efficacy of mycophenolate mofetil (MMF) on blood pressure (BP) is not well known. Identifying the performance of this drug will help to reduce the incidence of the adverse reactions. Methods: We systematically searched PubMed, MEDLINE, EMBASE, and the Cochrane Library for relevant studies published up to December, 2017. We compared blood pressure levels before and after the MMF treatment including systolic blood pressure and diastolic blood pressure. We used the Newcastle Ottawa scale for the assessment of the quality of studies. Analysis was performed using the statistical software Review Manager Version 5.0 and STATA 14.0. Result: We retrieved 6 studies with 208 patients. The data extracted were systolic BP (SBP) and diastolic BP (DBP). Study quality was assessed using the method of Jadad, and data were synthesized using a random-effects model and weight mean difference. MMF caused a small reduce in DBP (0.79mmHg, 95% CI, 0.03 to 1.55, P=0.043), with no obvious effect on SBP (0.12mmHg, 95%CI -0.41 to 0.64). In meta-regression, country (china vs. other country), duration of follow-up, percentage of men, and mean age of study participants were proved to be not the contributing factors. Conclusion: The MMF treatment can slightly reduce DBP, but not affect the SBP, which indicated the cardiovascular safety of this immunosuppressive agent.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.053 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".