Long-Acting Injectable Antipsychotics: Recommendations for Clinicians
Bibliographic record
Abstract
A major source of limitation to the real effectiveness of antipsychotics is the high rate of patient nonadherence or, more frequently, partial adherence. Using long-acting injectable (LAI) formulations is likely to reduce the impact of such adherence problems. Conversely, the use of LAIs in Canada remains low relative to many other jurisdictions. Based on effectiveness data from randomized control trials and other, less rigorous, studies, as well as our 2 qualitative studies exploring numerous issues around the use of LAIs, including their low use, we put forward 10 different recommendations for consideration by clinicians. These are also based on the experience of many clinicians and clinician scientists. These recommendations address mostly clinical challenges associated with the use of LAIs. Their application in clinical settings is illustrated in our report through several case examples highlighting the large variation across patients and different phases of illness. It is recommended that LAIs should be considered as a treatment option for psychotic disorders across all phases, including the first 2 to 5 critical years.
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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 | 0.025 | 0.094 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.036 | 0.029 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".