Aluminum toxicokinetics in peritoneal dialysis patients
Bibliographic record
Abstract
CONTEXT: Despite the risk of aluminum (Al) toxicity in dialysis patients, little is known about its toxicokinetics (TK) in this population. A national contamination of dialysate solutions with Al provided the opportunity to study Al TK in peritoneal dialysis (PD) patients and to better understand the influence of covariates on its disposition. METHODS: Al levels in serum and dialysate as well as other laboratory values were collected prospectively from 83 PD patients after correction of Al contamination. Population TK analyses were conducted with NONMEM VI using standard model discrimination criteria. Covariate analyses were also performed using stepwise forward regression followed by backward deletion. RESULTS: After correction of Al exposure, serum levels declined in a biphasic manner, which was captured by the TK model. The TK of Al were best described by a 2-compartment model with linear elimination. Total creatinine clearance was a significant covariate for total clearance (CL). Mean parameter estimates for volume of central compartment (V1), CL, volume of peripheral compartment (V2), volume of distribution at steady-state (Vss), and intercompartmental clearance (Q) were 168 L, 8.99 L/day, 12 000 L, 12 168 L, and 4.93 L/day, respectively. Inter-individual variability for CL and V2 were 22.6 and 51.1%, respectively. Al distributional half-life was 8.5 days, while the terminal elimination half-life was 7.2 years. This model confirms that the large Vss reflects the widespread distribution of Al in bone, lungs, liver, and other tissues. CONCLUSION: This study describes the first population Al TK model in a large group of PD patients, which includes a covariate effect. The model confirms the extensive half-life and tissue distribution of Al in a dialysis-dependent population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".