Trimetazidine reduces contrast-induced nephropathy in patients with renal insufficiency undergoing coronary angiography and angioplasty
Bibliographic record
Abstract
OBJECTIVES: This systematic review and meta-analysis assesses the utility of trimetazidine (TMZ) to prevent contrast induced nephropathy (CIN) in patients with renal insufficiency undergoing coronary angiography and angioplasty. MATERIALS AND METHODS: This meta-analysis was formulated and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search of databases was conducted by 2 researchers independently for clinical trials, comparing hydration plus TMZ vs conventional hydration alone for prevention of CIN through January 2020. All patients had renal insufficiency (defined as GFR < 89 ml/minute/1.73 m2) and the outcome of interest was the incidence of contrast induced acute kidney injury. The odds ratio (OR) was estimated with 95% confidence interval (CI). Heterogeneity was reported with the I2 statistic, using a fixed-effects model, and >50% of I2 was considered to be statistically significant. RESULTS: Eleven studies, 1611 patients, met the inclusion/exclusion criteria: 797 patients comprised the TMZ plus hydration group and the remaining 814 patients comprised the control (hydration only) group. Heterogeneity was low I2 = 0%, P = .84, and the heterogeneity of each study was also low. The incidence of CIN in the TMZ plus hydration group was 6.6% (53/797), while the incidence of CIN in the control (hydration only) group was 20% (165/814). Pooled analysis of all studies showed TMZ reduced incidence of CIN compared to saline hydration alone (OR risk 0.30, 95% CI 0.21, 0.42, P < .0001). CONCLUSION: TMZ added to hydration reduces CIN in renal insufficiency patients undergoing coronary angiography.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".