The role of N-acetylcysteine in preventing radiographic contrast-induced nephropathy
Bibliographic record
Abstract
INTRODUCTION: There have been many small randomized controlled trials evaluating the effectiveness of N-acetylcysteine (NAC) in preventing radiographic contrast-induced nephropathy. Most studies have suggested a beneficial NAC effect. This meta-analysis describes the effect of NAC in the prevention of radiographic contrast-induced nephropathy in the aggregated trial data. METHODS: A search using MEDLINE from 1966 to December 2003 identified all randomized control trials that evaluated NAC in those patients at risk of acute renal failure (ARF) following either angiographic or CT scan contrast exposure. All studies included in the review employed the use of either low-osmolar (n = 9 trials) or iso-osmolar (n = 2 trials) contrast agents. The outcome of interest was ARF as defined by a rise in serum creatinine (Cr > or = 0.5 mg/dl rise or > 25% increase from baseline) after exposure to contrast. The data were aggregated by the methods of Mantel and Haenszel. RESULTS: The overall summary odds ratio estimate of 0.46 (95% confidence interval 0.32 - 0.66) suggests a strong protective effect of NAC in preventing radiographic-induced nephropathy. CONCLUSION: In summary, there is good aggregate trial evidence to suggest that patients who have an elevated serum creatinine level at baseline benefit from receiving periprocedure NAC in the prevention of contrast-induced ARF.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".