Antipsychotic adjunctive therapy to mood stabilizers and 1‐year rehospitalization rates in bipolar disorder: A cohort study
Bibliographic record
Abstract
OBJECTIVES: Antipsychotic adjunctive therapy to mood stabilizers (MSs) may improve relapse prevention; however, only a few naturalistic studies, reflecting more generalizable bipolar disorder (BD) samples, support this notion. We compared the 1-year rehospitalization rates of manic patients with bipolar I disorder (BD-I) who were discharged with MS (lithium or valproate) monotherapy or with adjunctive atypical or typical antipsychotic therapy. METHODS: A total of 201 patients with BD-I who were hospitalized with manic episodes between 2005 and 2013 were retrospectively followed for 1-year rehospitalization rates according to treatment at discharge: MS monotherapy, MS with atypical antipsychotics, and MS with typical antipsychotics. Additionally, time to rehospitalization during the 1-year period after discharge was compared between treatment groups. Multivariable survival analyses adjusted for covariates known to influence rehospitalization were conducted. RESULTS: Rehospitalization rates within 1 year were significantly lower in the MS with atypical antipsychotics group (6.3%) compared to the MS monotherapy group (24.3%, P=.008) and to the MS with typical antipsychotics group (20.6%, P=.02). Time to rehospitalization was significantly longer for the MS with atypical antipsychotics group (345.5 days) compared to the MS monotherapy group (315.1 days, P=.006) and to the MS with typical antipsychotics group (334.1 days, P=.02). The MS with atypical antipsychotics group had a significantly reduced adjusted risk of rehospitalization (hazard ratio=0.17, 95% confidence interval: 0.05-0.61, P=.007) compared to the MS monotherapy group. CONCLUSIONS: Atypical antipsychotic adjunctive therapy to MSs may be more effective than MS monotherapy in preventing rehospitalization during the 1-year period after a BD manic episode.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".