Trajectories of adherence to mood stabilizers in patients with bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Nonadherence with mood stabilizers is a major problem that negatively impacts the course of bipolar disorder. Medication adherence is a complex individual behavior, and adherence rates often change over time. This study asked if distinct classes of adherence trajectories with mood stabilizers over time could be found, and if so, which patient characteristics were associated with the classes. METHODS: This analysis was based on 12 weeks of daily self-reported data from 273 patients with bipolar 1 or II disorder using ChronoRecord computer software. All patients were taking at least one mood stabilizer. The latent class mixed model was used to detect trajectories of adherence based on 12 weekly calculated adherence datapoints per patient. RESULTS: Two distinct trajectory classes were found: an adherent class (210 patients; 77%) and a less adherent class (63 patients; 23%). The characteristics associated with the less adherent class were: more time not euthymic (p < 0.001) and female gender (p = 0.016). No other demographic associations were found. CONCLUSION: In a sample of motivated patients who complete daily mood charting, about one quarter were in the less adherent class. Even patients who actively participate in their care, such as by daily mood charting, may be nonadherent. Demographic characteristics may not be useful in assessing individual adherence. Future research on longitudinal adherence patterns in bipolar disorder is needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".