Patterns of atypical antipsychotic therapy use in adults with bipolar I disorder in the USA
Bibliographic record
Abstract
OBJECTIVES: This study aims to describe the utilization patterns of atypical antipsychotics (AA) among insured patients with bipolar I disorder in the USA. METHODS: We studied patients with bipolar I disorder who newly initiated an oral AA between 2002 and 2008. Utilization measures included adherence [medication possession ratio (MPR) ≥80%], persistence (gaps ≤15 days between refills and an absence of ≥30 days of continuous concomitant non-index AA use), non-compliance (16-29 day gaps with no evidence of switch/augmentation), and discontinuation of the index AA (≥30 days without index AA, no evidence of switch/augmentation). RESULTS: The study included 16 807 patients: mean age 43.3 years, 67.7% female. Overall, adherence to the index AA was low (8.3%; mean MPR = 0.2). Only 10.5% of the patients were persistent to index AA, another 13.6% were non-compliant, and 63.4% discontinued index AA with an average time to discontinuation of 66 days. A majority (69.5%) of the discontinued patients did not resume any antipsychotic therapy. Results were similar across insurance types and index AA. CONCLUSION: Adherence to and persistence with AA treatment were low in new users with bipolar I disorder. Most patients discontinued the index AA and did not restart any antipsychotic treatment. Future study should distinguish physician-directed discontinuation versus patient non-adherence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".