Sex-Specific Associations Between Bipolar Disorder Pharmacological Maintenance Therapies and Inpatient Rehospitalizations: A 9-Year Swedish National Registry Study
Bibliographic record
Abstract
Background: Long-term pharmacological maintenance therapy is often essential among people with bipolar disorder to reduce the need for inpatient care. Sex-specific responses to maintenance therapies are expected but remain largely unknown. Here, we examined for sex-specific associations between common maintenance therapies for bipolar disorder with inpatient rehospitalizations following patients’ index discharges during 2006-2014. Methods: Population-based data on maintenance therapies and rehospitalizations were extracted from Swedish national registries. We adopted the within-individual design to compare the time on- versus off- maintenance therapy for males and females, respectively. Extended stratified Cox proportional hazards regression models were employed to quantify the rate of rehospitalization as a function of common maintenance drugs and other important time-varying control variables. Results: Our primary analysis included 22,681 bipolar disorder rehospitalizations by 6,400 males and 9,588 (60.0%) females over an observation time of 62,813 person-years. The time spent on- versus off- maintenance lithium, lamotrigine, quetiapine, or olanzapine was statistically significant upon adjustment among either sex for reducing the rate of bipolar rehospitalizations. Adjusted sex-specific statistically significant associations were also observed. Among females, the time on- (versus off-) long-acting injectable risperidone reduced the rate of bipolar rehospitalizations by 73% (56-84%), carbamazepine by 44% (18-62%), aripiprazole by 29% (13-42%), and valproate by 23% (11-33%); whereas among males, ziprasidone by 65% (41-79%). Conclusion: The effectiveness of most maintenance therapies is generally comparable and uniform among both males and females. Despite some statistically significant sex-specific associations, estimates for each drug were fairly consistent between sexes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".