The association between concurrent psychotropic medications and self‐reported adherence with taking a mood stabilizer in bipolar disorder
Bibliographic record
Abstract
OBJECTIVE: Multiple psychotropic medications are routinely prescribed to treat bipolar disorder, creating complex medication regimens. This study investigated whether the daily number of psychotropic medications or the daily number of pills were associated with self-reported adherence with taking a mood stabilizer. METHODS: Patients self-reported their mood and medications taken daily for about 6 months. Adherence was defined as taking at least one pill of any mood stabilizer daily. Univariate general linear models (GLMs) were used to estimate if adherence was associated with the number of daily medications and the number of pills, controlling for age. The association between mean daily dosage of mood stabilizer and adherence was also estimated using a GLM. RESULTS: Three hundred and twelve patients (mean age 38.4 +/- 10.9 years) returned 58,106 days of data and took a mean of 3.1 +/- 1.6 psychotropic medications daily (7.0 +/- 4.2 pills). No significant association was found between either the daily number of medications or the daily number of pills and adherence. For most mood stabilizers, patients with lower adherence took a significantly smaller mean daily dosage. CONCLUSIONS: The number of concurrent psychotropic medications may not be associated with adherence in bipolar disorder. Patients with lower adherence may be taking smaller dosages of mood stabilizers.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".