Second-Generation Long-Acting Injectable Antipsychotics and the Risk of Treatment Failure in a Population-Based Cohort
Bibliographic record
Abstract
Introduction: Second-generation long-acting injectable antipsychotics (SG-LAIAs) may improve outcomes compared to other antipsychotics. Real-world studies using linked administrative databases play an important role in assessing the comparative effectiveness of antipsychotic medications. Methods: We used a prevalent new-user design in a population-based cohort of antipsychotic users with diagnosis of a psychotic disorder to compare the primary outcome of treatment failure, defined as psychiatric hospitalization, completed suicide, incarceration, or treatment discontinuation. Additional outcomes were all-cause mortality. SG-LAIA users were matched on a 1:1 basis with other antipsychotic users based on the time-conditional propensity score, calendar time, and prior antipsychotic exposure. Results: The use of LAIAs was not associated with a lower risk of treatment failure than other antipsychotics (adjusted hazard ratio 1.07 and 95% confidence interval 0.98–1.15) but did reduce all-cause mortality (adjusted hazard ratio 0.69 and 95% confidence interval 0.48–0.99). Monotherapy with LAIAs was superior to other antipsychotic monotherapy (adjusted hazard ratio for treatment failure 0.83 and 95% confidence interval 0.78–0.89), and LAIAs were superior to other antipsychotics in antipsychotic-naïve users (adjusted hazard ratio for treatment failure 0.57 and 95% confidence interval 0.47–0.70). Conclusion: In this population-based cohort, SG-LAIAs reduced the risk of treatment failure in incident new users but not in prevalent new users.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".