Quinine pharmacokinetics in chronic haemodialysis patients
Bibliographic record
Abstract
AIMS: Quinine is often used to prevent muscle cramps in patients with chronic renal failure. A standard dose of 300 mg at bedtime is usually recommended, but little is known about the pharmacokinetics of quinine in the presence of renal failure. METHODS: We studied the pharmacokinetics of quinine in eight normal subjects and eight patients with chronic renal failure on haemodialysis after a single oral dose of quinine sulphate (300 mg). RESULTS: The concentration of alpha1-acid glycoprotein (AAG), the major binding protein for quinine, was increased in haemodialysis patients compared with control subjects (1.52 g l-1 vs 0.63 g l-1 [mean difference 1.033; 95% CI 0.735, 1.330]) whereas albumin levels were decreased (30 g l-1 vs 40 g l-1 [mean difference 9.5; 95% CI 3.048, 15.952]). Accordingly, the free fraction of quinine was decreased (0.024 vs 0.063 [mean difference 0.0380; 95% CI 0.0221, 0.0539]) and the apparent volume of distribution tended to decrease (0.95 l kg-1 vs 1.43 l kg-1 [mean difference 0.480; 95% CI 0.193, 1.154]). The quinine binding ratio correlated with the plasma concentration of AAG but not that of albumin. The clearance of free (unbound) quinine was increased in haemodialysis patients compared with controls (67.9 ml min-1 kg-1 vs 41.1 ml min-1 kg-1 [mean difference -26.8; 95% CI, -56.994, 3.469]), and the area under the curve (AUC) of the two main metabolites, 3-hydroxyquinine and 10,11-dihydroxydihydroquinine were increased. CONCLUSIONS: In patients with chronic renal failure, there is an increase in plasma protein binding and in the clearance of free drug, resulting in lower plasma concentration of free quinine.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".