Dose and Dosing Frequency of Long-Acting Injectable Antipsychotics
Bibliographic record
Abstract
Brain imaging data of antipsychotics have mainly been derived from oral antipsychotic drugs, which hampers our understanding of the requirement of dose/dosing frequency of long-acting injectable (LAI) antipsychotics for the maintenance treatment of schizophrenia. A systematic literature search was performed to identify positron emission tomography (PET) and single-photon emission computed tomography (SPECT) studies that assessed dopamine D2 receptor occupancy levels with LAI antipsychotic drugs in humans, using PubMed, EMBASE, and PsycINFO (last search, February 2013). Twenty studies (15 PET and 5 SPECT studies) were identified. The most investigated drug in these PET and SPECT studies was haloperidol decanoate (44 subjects; 11 studies), followed by risperidone LAI (24 subjects; 3 studies), olanzapine pamoate (14 subject; 1 study), and fluphenazine decanoate (12 subjects; 3 studies). The data have demonstrated high and continuous D2 receptor blockade with LAIs; the effects of LAI first-generation antipsychotics on the central nervous system may persist for several months. The prospective and cross-sectional studies showed that continuous dopamine D2 receptor blockade above 65% (ie, the lower end of the established therapeutic window for the acute phase treatment) was not always necessary for maintenance treatment for at least some of the patients. In conclusion, because of the limited brain imaging data on LAI antipsychotics, we still do not know the best way to dose them. Still, the currently available brain imaging data raises a possibility that the dosing interval of LAI antipsychotics may be extended beyond the currently indicated range in some patients.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".