Impact of atypical long-acting injectable versus oral antipsychotics on rehospitalization rates and emergency room visits among relapsed schizophrenia patients: a retrospective database analysis
Bibliographic record
Abstract
BACKGROUND: Among schizophrenia patients relapsed on an oral antipsychotic (AP), this study compared the impact of switching to atypical AP long-acting injectable therapy (LAT) versus continuing oral APs on hospitalization and emergency room (ER) visit recurrence. METHODS: Electronic records from the Premier Hospital Database (2006-2010) were analyzed. Adult patients receiving oral APs during a schizophrenia-related hospitalization were identified and, upon relapse (i.e., rehospitalization for schizophrenia), were stratified into (a) patients switching to atypical LAT and (b) patients continuing with oral APs. Atypical LAT relapse patients were matched 1:3 with oral AP relapse patients, using a propensity score model. Andersen-Gill Cox proportional hazards models assessed the impact of atypical LAT versus oral AP on time to multiple recurrences of all-cause hospitalizations and ER visits. No adjustment was made for multiplicity. RESULTS: Atypical LAT (N = 1032) and oral AP (N = 2796) patients were matched and well-balanced with respect to demographic (mean age: 42.1 vs 42.4 years, p = .5622; gender: 43.6% vs 44.6% female, p = .5345), clinical, and hospital characteristics. Over a mean 30-month follow-up period, atypical LATs were associated with significantly lower mean number of rehospitalizations (1.25 vs 1.61, p < .0001) and ER visits (2.33 vs 2.67, p = .0158) compared with oral APs, as well as fewer days in hospital (mean days: 13.46 vs. 15.69, p = .0081). Rehospitalization (HR 0.81, 95% CI 0.76-0.87, p < .0001) and ER visit (HR 0.88, 95% CI 0.87-0.93, p < .0001) rates were significantly lower for patients receiving atypical LAT versus oral APs. CONCLUSIONS: This hospital database analysis found that in relapsed schizophrenia patients, atypical LATs were associated with lower rehospitalization and ER visit rates than oral APs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".