N-Acetylcysteine does not artifactually lower plasma creatinine concentration
Bibliographic record
Abstract
BACKGROUND: All randomized controlled trials of N-acetylcysteine (NAC) in contrast media-induced nephropathy used creatinine as a marker of renal function. However, it has been suggested that NAC may lower plasma creatinine levels independent of any effects on glomerular filtration rate (GFR). METHODS: At a tertiary hospital 110 cardiac surgical patients were randomly allocated to peri-operative infusion of NAC (300 mg/kg over 24 h, N = 30) or placebo (N = 80). We compared the plasma concentrations of creatinine, cystatin C and urea, the plasma creatinine/plasma cystatin C ratio and the estimated GFR at baseline and at 24 and 72 h after commencement of the infusion. We measured urinary creatinine concentration at 24 h. RESULTS: At baseline, the plasma creatinine/plasma cystatin C ratio did not differ between the NAC and placebo group (0.90 versus 0.92; P = 0.94). There was no significant difference in the plasma creatinine/plasma cystatin C ratio for the NAC and placebo group either during or after NAC infusion at 24 h (1.03 versus 1.00; P = 0.78) and 72 h (0.94 versus 0.89; P = 0.09). Those allocated to NAC showed no difference in urinary creatinine excretion when compared to placebo (P = 0.24). CONCLUSIONS: The results of our study do not demonstrate that NAC artifactually lowers creatinine measured using the Jaffé method. (ClinicalTrials.gov, NCT00332631, NCT00334191).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".