Evaluating cimetidine for GFR estimation in liver transplant recipients
Bibliographic record
Abstract
Background. Serum creatinine (Scr)-based equations lack accuracy in predicting glomerular filtration rate (GFR) in patients with liver disease. Cimetidine has been shown to improve the performance of Scr-based GFR formulae. Methods. We evaluated the use of cimetidine on the performance of GFR-estimating equations in 39 liver transplant recipients. The patients received oral cimetidine (800 mg tid) during a 24-h urine collection. The next day, the patients underwent radionucleotide GFR (rGFR) determination and Scr was measured for creatinine clearance (CrCl) and GFR estimation using the Cockcroft-Gault, Nankivell and modified diet in renal disease (MDRD) equations. Data were analysed using the Pearson correlation statistic and Bland-Altman plots. Results. The mean rGFR was 65 +/- 26.4 mL/min. The use of cimetidine increased the bias between rGFR and the Nankivell and MDRD equations. The combined root mean square error for the CrCl, Cockcroft-Gault, Nankivell and MDRD equations without cimetidine were 20.2, 15.6, 17.0 and 15.5 and cimetidine-aided were 28.2, 23.2, 23.7 and 24.3, respectively. Conclusions. All the tested equations without using cimetidine predicted GFR with modest accuracy. The addition of cimetidine decreased the precision and increased the bias of all the GFR-estimating equations. In the absence of accurate GFR-estimating equations, rGFR should be used to monitor kidney function in liver transplant recipients.
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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 | 0.003 | 0.014 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".