Prediction Model of Serum Lithium Concentrations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
INTRODUCTION: Therapeutic drug monitoring is necessary for lithium, but clinical application of several prediction strategies is still limited because of insufficient predictive accuracy. We herein proposed a suitable model, using creatinine clearance (CLcr)-based lithium clearance (Li-CL). METHODS: Patients receiving lithium provided the following information: serum lithium and creatinine concentrations, time of blood draw, dosing regimen, concomitant medications, and demographics. Li-CL was calculated as a daily dose per trough concentration for each subject, and the mean of Li-CL/CLcr was used to estimate Li-CL for another 30 subjects. Serum lithium concentrations at the time of sampling were estimated by 1-compartment model with Li-CL, fixed distribution volume (0.79 L/kg), and absorption rate (1.5/hour) in the 30 subjects. RESULTS: One hundred thirty-one samples from 82 subjects (44 men; mean±standard deviation age: 51.4±16.0 years; body weight: 64.6±13.8 kg; serum creatinine: 0.78±0.20 mg/dL; dose of lithium: 680.2±289.1 mg/day) were used to develop the pharmacokinetic model. The mean±standard deviation (95% confidence interval) of absolute error was 0.13±0.09 (0.10-0.16) mEq/L. DISCUSSION: Serum concentrations of lithium can be predicted from oral dosage with high precision, using our prediction model.
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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) | 0.000 | 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 it