N-acetylcysteine and Contrast-Induced AKI: An Umbrella Review of Systematic Reviews
Bibliographic record
Abstract
Background: There have been numerous trials and metaanalyses of n-acetylcysteine (NAC) in contrast-induced acute kidney injury (CI-AKI). The large trials do demonstrate the futility of NAC. In this umbrella review, we synthesize the evidence as collated from the systematic reviews and metanalyses. Methods: A literature search was done to identify all systematic reviews on NAC and CI-AKI using databases from inception to end 2020. Two independent reviewers screened the studies and extracted data on including assessment of heterogeneity, publication bias and we used the A MeaSurement Tool to Assess systematic Reviews (AMSTAR 2) to appraise the included studies. Results: The literature search retrieved 273 citations, of which 42 systematic reviews were eligible. The quality assessment using the AMSTAR-2 was variable (see table) with high quality noted for certain domains (eg explicit question, explanation of study designs), low for others (funding, reasons and list of excluded studies). All studies reported high heterogeneity; 39/42 (93%) performed a meta-analysis, all with an overall benefit with NAC (pooled relative risks range 0.38 - 0.84). 26/42 (62%) reported on the presence of publication bias, and 31/42 (74%) reported the risk of bias. Only 2/42 studies (5%) reported on efforts to resolve heterogeneity did not report a summary effect size as a result. Conclusions: Systematic reviews can provide misleading results if heterogeneity and publication bias are not taken into account.AMSTAR Checklist
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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 | 0.054 | 0.189 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.038 | 0.028 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".