Diltiazem as augmentation therapy in patients with treatment-resistant bipolar disorder: a retrospective study.
Bibliographic record
Abstract
OBJECTIVE: To examine the efficacy of a slow-release formulation of diltiazem as adjunctive therapy in patients with treatment-resistant bipolar disorder. DESIGN: Retrospective study. PATIENTS: Eight female patients with treatment-resistant bipolar disorder. INTERVENTIONS: Patients were administered diltiazem and monitored for a 6-month period before starting diltiazem and a 6-month period after starting the drug. OUTCOME MEASURES: All patients were seen at least monthly and usually every 2 weeks. The frequency and severity of both depressive and manic episodes were examined during the 6-month period after starting diltiazem, and compared with those during the 6-month period before diltiazem treatment. RESULTS: There was a statistically significant decrease in the frequency and severity of both manic and depressive episodes in these patients after they started treatment with diltiazem, compared with the period before they started treatment with diltiazem (p < 0.001). There was no evidence of side effects requiring patient withdrawal or of drug interactions. CONCLUSIONS: The results support previous suggestions that calcium-channel antagonists may be an effective adjunctive treatment in the management of bipolar disorder. Further controlled clinical studies are needed to confirm this small, open-label, retrospective study.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".