International Consensus Study of Antipsychotic Dosing
Bibliographic record
Abstract
OBJECTIVE: Potency equivalents for anti-psychotic drugs are required to guide clinical dosing and for designing and interpreting research studies. Available dosing guidelines are limited by the methods and data from which they were generated. METHOD: With a two-step Delphi method, the authors surveyed a diverse group of international clinical and research experts, seeking consensus regarding antipsychotic dosing. The authors determined median clinical dosing equivalents and recommended starting, target range, and maximum doses for 61 drugs, adjusted for selected clinical circumstances. RESULTS: Participants (N=43) from 18 countries provided dosing recommendations regarding treatment of psychotic disorders for 37 oral agents and 14 short-acting and 10 long-acting parenteral agents. With olanzapine 20 mg/day as reference, estimated clinical equivalency ratios of oral agents ranged from 0.025 for sulpiride to 10.0 for trifluperidol. Seventeen patient and treatment characteristics, including age, hepatic and renal function, illness stage and severity, sex, and diagnosis, were associated with dosing modifications. CONCLUSIONS: In the absence of adequate prospective, randomized drug-drug comparisons, the present findings provide broad, international, expert consensus-based recommendations for most clinically employed antipsychotic drugs. They can support clinical practice, trial design, and interpretation of comparative antipsychotic trials.
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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 | 0.290 | 0.449 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".