Rapid tranquilization with olanzapine in acute psychosis: a case series.
Bibliographic record
Abstract
Acute, high-dose loading strategies (rapid neuroleptization) with the first-generation antipsychotics administered orally or parenterally, alone or combined with benzodiazepines, have been a commonly used treatment paradigm for controlling acutely agitated psychotic patients. The rationale was to achieve high plasma levels of drug within a shorter time period, resulting in rapid symptom mitigation. However, studies have shown that rapid neuroleptization with first-generation antipsychotics is associated with a greater incidence of side effects. To our knowledge, loading strategies with second-generation antipsychotics have not been investigated, primarily owing to a need for dose titration. Olanzapine, a second-generation antipsychotic, is well tolerated in doses ranging from 5 to 20 mg. The objective of this report was to determine experience with the use of up to 20 mg of an oral loading dose of olanzapine administered within 4 hours in the treatment of patients early in an acute psychotic phase of their illness. In the reported case series of 57 patients, olanzapine initiated at 15 to 20 mg/day was a safe and effective medication for rapidly calming the agitation of acutely agitated psychotic patients (rapid tranquilization). Furthermore, dose reduction over 2 to 3 weeks was achieved in a number of patients without appreciable loss of efficacy.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".