Drug treatment patterns in bipolar disorder: analysis of long-term self-reported data
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The objective of this study is to investigate drug treatment patterns in bipolar disorder using daily data from patients who received treatment as usual. METHODS: Patients self-reported the drugs taken daily for about 6 months. Daily drug use and drug combinations were determined for each patient, both by the specific drugs and by medication class. The drug load was calculated for all drugs taken within a medication class. RESULTS AND DISCUSSION: Four hundred fifty patients returned a total of 99,895 days of data (mean 222.0 days). The most frequently taken drugs were mood stabilizers. Of the 450 patients, 353 (78.4%) took a stable drug combination for ≥50% of days. The majority of patients were taking polypharmacy, including 75% of those with a stable combination. Only a small number of drugs were commonly taken within each medication class, but there were a large number of unique drug combinations: 52 by medication class and 231 by specific drugs. Eighty percent of patients with a stable combination were taking three or less drugs daily. Patients without a stable combination took drugs but made frequent changes. Taking more than one drug within a medication class greatly increased the drug load. To summarize, (1) patients were more likely to take a mood stabilizer than any other drug; (2) although most patients were taking polypharmacy, there were no predominant drug regimens even among those taking a stable combination; and (3) most patients with a stable combination take a relatively small number of drugs daily. The wide variation in drug regimens and numerous possible drug combinations suggest that more evidence is needed to optimize treatment of bipolar disorder.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it