Accuracy of the Pepin method to determine appropriate lithium dosages in healthy volunteers.
Bibliographic record
Abstract
OBJECTIVE: To assess if the lithium dosage prescribed according to the Pepin method leads to therapeutic serum concentrations of lithium. METHODS: For 13 healthy volunteers, the initial daily doses of lithium were calculated according to the Pepin formula with a view to obtaining a serum lithium level of 0.8 mmol/L. Lithium was administered twice daily for 21 days, and blood samples were drawn daily, 12 hours after the last dose was taken. Dosage was adjusted if serum concentrations were below 0.6 mmol/L or above 1.0 mmol/L or if major side effects were reported. RESULTS: Daily lithium doses ranged from 1050 mg to 1950 mg (mean 1569 mg, standard deviation [SD] 291 mg), The mean serum lithium concentrations for weeks 1, 2 and 3 were 0.74 mmol/L (SD 0.19 mmol/L), 0.67 mmol/L (SD 0.22 mmol/L) and 0.69 mmol/L (SD 0.13 mmol/L), respectively. Within-subject variance was negligible. Sixty-eight percent of the serum lithium concentration measurements fell between 0.57 mmol/L and 0.83 mmol/L, and 84% fell within the recommended therapeutic range of 0.60 mmol/L and 1.20 mmol/L. CONCLUSIONS: The Pepin method is a safe but conservative method for predicting the appropriate daily dose of lithium.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".