An Updated Review of the Optimal Lithium Dosage Regimen for Renal Protection
Bibliographic record
Abstract
OBJECTIVE: Despite several decades of research, there is still uncertainty regarding the optimal lithium dosage regimen associated with a decreased risk of renal effects, such as polyuria, in patients with bipolar affective disorder. We present an updated review of the literature to provide an informed dosing regimen recommendation for prescribers. METHOD: Major databases MEDLINE and Embase were searched using terms, such as lithium, drug administration schedule, dose-response relationship, once daily, twice daily, and sustained release. In addition, the bibliographies of related publications were manually searched. RESULTS: A total of 20 trials were included. Some trials showed a reduction in urine volume with single daily dosing (SDD), while others showed no change. The only trial evaluating patients newly started on lithium found a reduction in urinary frequency with SDD after 21 days. Trials examining renal biopsy results found that multiple daily doses were associated with more pathologic damage to the kidneys. SDD regimens were generally well tolerated, and no reduction in efficacy was noted in any of the trials. CONCLUSIONS: The available evidence is contradictory as to whether SDD of lithium reduces polyuria; however, no trial has demonstrated any downfall of SDD in terms of prophylactic efficacy or adverse effects. Given the added benefits of SDD, such as improved compliance, we recommend patients newly started on lithium should be converted to a SDD of lithium at bedtime once an appropriate daily dose is determined.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".