Regularity in Daily Mood Stabilizer Dosage Taken by Patients with Bipolar Disorder
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to investigate regularity in the daily mood stabilizer dosage taken by patients with bipolar disorder, and identify factors associated with irregularity. METHODS: Self-reported daily mood and medication data were available from 206 patients who took the same mood stabilizer for ≥100 days. Approximate entropy (ApEn) was used to measure serial regularity in daily mood stabilizer dosage. Generalized estimating equations (GEE) were used to estimate if demographic or clinical variables were associated with ApEn. RESULTS: There was a wide range of regularity in the daily mood stabilizer dosage. The mean percent of days of missing doses was 13.6%. The number of psychotropic medications (p=0.007), pill burden (p=0.004) and percent of days with depressed mood (p=0.013) were associated with more irregularity, while the percent of days euthymic (p=0.014) was associated with less irregularity. The percent of days missing doses was not associated with the number of medications, pill burden or mood ratings. DISCUSSION: Patients may have irregularity in daily dosage in spite of a low percent of days missing doses. Psychotropic medication regimen complexity and depression are associated with increased dosage irregularity. Research is needed on how irregularity in daily dosage impacts the continuity of drug action of mood stabilizers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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".