Effect of N‐acetylcysteine on renal function in patients with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND: N-acetylcysteine (NAC) is commonly administered to high-risk individuals to attenuate the risk of contrast-induced nephropathy in spite of the debate regarding its efficacy. In several studies serum creatinine decreased after exposure to NAC and contrast dye. The mechanism by which NAC attenuates the decline in renal function is not known. Studies in subjects with normal renal function suggest NAC may have an effect on tubular secretion. AIM: The aim of this study was to determine the effect of NAC on renal function, measured by serum creatinine and Cystatin C, in patients with stage 3 chronic kidney disease. METHOD: Serum creatinine and Cystatin C were measured prior to, 4, 24 and 48 h after the administration of 600 mg oral NAC in 30 patients. The protocol was repeated with the addition of 1200 mg oral cimetidine administered 3 h before NAC. RESULTS: Serum creatinine was not significantly different from baseline (186 +/- 65 micromol/L) to 4 h (185 +/- 62 micromol/L), 24 h (187 +/- 64 micromol/L) or 48 h (184 +/- 61 micromol/L) post NAC, nor were Cystatin C levels. Co-administration of cimetidine resulted in a significant rise in serum creatinine with no change in Cystatin C levels. CONCLUSION: This study failed to detect a change in serum creatinine or Cystatin C after a single dose of NAC in participants with stage 3 chronic kidney disease. Further randomized trials of multiple doses and longer follow up are needed to confirm these results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".