Pattern of Pharmacotherapy by Episode Types for Patients With Bipolar Disorders and Its Concordance With Treatment Guidelines
Bibliographic record
Abstract
This study aimed to investigate the overall prescription pattern for patients with bipolar disorders in Korea and its relevance to the practice guidelines. Prescription records from all patients with bipolar I and II disorders who have been admitted or who started the outpatient treatment during the year of 2009 in 10 academic setting hospitals were reviewed. A total of 1447 patients with bipolar I and II disorders were included in this study. Longitudinal prescription patterns of inpatients and outpatients were analyzed by episode types and compared with the clinical practice guideline algorithms. In all phases, polypharmacy was chosen as an initial treatment strategy (>80%). The combination of mood stabilizer and atypical antipsychotics was the most favored. Antipsychotics were prescribed in more than 80% of subjects across all phases. The rate of antidepressant use ranged from 15% to 40%, and it was more frequently used in acute treatment and bipolar II subjects. The concordance rate of prescriptions for manic inpatients to the guidelines was higher and relatively more consistent (43.8%-48.7%) compared with that for depressive inpatients (18.6%-46.9%). Polypharmacy was the most common reason for nonconcordance. In Korean psychiatric academic setting, polypharmacy and atypical antipsychotics were prominently favored in the treatment of bipolar disorder, even with the lack of evidence of its superiority. More evidence is needed to establish suitable treatment strategies. In particular, the treatment strategy for acute bipolar depression awaits more consensuses.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".