<i><scp>N</scp></i>‐acetylcysteine may improve residual renal function in hemodialysis patients: A pilot study
Bibliographic record
Abstract
Clinical outcomes in chronic dialysis patients are highly dependent on preservation of residual renal function (RRF). N-acetylcysteine (NAC) may have a positive effect on renal function in the setting of nephrotoxic contrast media administration. In our recent study, we showed that NAC may improve RRF in peritoneal dialysis patients. The aim of the present study was to investigate the effect of NAC on RRF in patients treated with chronic hemodialysis. Prevalent chronic hemodialysis patients with a residual urine output of at least 100 mL/24 hours were included. The patients were administered oral NAC 1200 mg twice daily for 2 weeks. Residual renal function was assessed at baseline and at the end of treatment using a midweek interdialytic urine collection for measurement of urine output and calculation of residual renal Kt/V and glomerular filtration rate (GFR). Residual GFR was measured as the mean of urea and creatinine residual renal clearance. Each patient served as his own control. Twenty patients were prospectively enrolled in the study. Administration of NAC 1200 mg twice daily for 2 weeks resulted in significant improvement in RRF: urine volume increased from 320 ± 199 to 430 ± 232 mL/24 hours (P < 0.01), residual renal Kt/V increased from 0.19 ± 0.12 to 0.29 ± 0.14 (P < 0.01), and residual GFR increased from 1.6 ± 1.6 to 2.4 ± 2.3 mL/minute/1.73 m(2) (P < 0.01). N-acetylcysteine may improve RRF in patients treated with chronic hemodialysis.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".