Use of polypharmacy and self-reported mood in outpatients with bipolar disorder
Bibliographic record
Abstract
Objective. As polypharmacy is routinely used for the treatment of bipolar disorder, the relation between the daily number of psychotropic medications and self-reported mood was investigated. Method. Eighty patients (35 men and 45 women) with a diagnosis of bipolar disorder I or II, recruited from academic centres, entered their mood, sleep, and psychotropic medications for 3 months into ChronoRecord software. A total of 8662 days of data was received (mean 114.7 days/per patient). Results. Seventy-nine patients took a mean of 3.8 medications daily (SD 1.7; range 1-9); one took none. Of these patients, 73 (92.4%) took mood stabilizers, 47 (58.8%) took antidepressants, 31 (38.8%) took antipsychotics, 34 (42.5%) took benzodiazepines and 17 (21.1%) took thyroid hormones. Patients reporting normal mood more frequently took fewer medications; the Pearson correlation coefficient between the number of medications and the percent of days normal was -0.481 (P < 0.001). Grouping by number of medications, ANOVA analysis showed those taking fewer medications reported normal mood more frequently (P<0.001). Conclusion. Combination treatment regimens are routinely prescribed for bipolar disorder. Patients reporting normal mood more frequently took a fewer number of daily medications. Studies are needed to better identify those patients who would benefit from polypharmacy and to optimise the combinations of medications for patients with refractory disorder.
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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 | 0.004 |
| 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) | 0.002 | 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".