Depot risperidone in the outpatient management of bipolar disorder: a 2-year study of 10 patients
Bibliographic record
Abstract
Nonadherence with pharmacotherapy occurs frequently in bipolar patients, and is a common cause of relapse. Depot formulations of first-generation antipsychotic medications have been shown to reduce manic relapses during maintenance therapy in bipolar patients, but appear to increase liability for depressive episodes. A depot formulation of risperidone has recently become commercially available, but to date there is little evidence regarding its efficacy or safety in bipolar patients. Ten outpatients with bipolar I or II disorder, with a predominantly depressive course of illness, were prescribed risperidone Consta as an adjunct to mood stabilizing and other medications in routine clinical practice, and were followed during 2 years of maintenance therapy. The number of mood episodes, including depressive episodes, decreased in all patients compared with an equivalent pretreatment period. No patient required hospitalization for a mood episode. The number and doses of concomitant medications was reduced in most patients risperidone Consta was well tolerated, with minimal to modest weight gain, absent or reduced extrapyramidal symptoms, and few other side effects. Clinicians may consider risperidone Consta as an option in patients with refractory bipolar illness, including those with a predominantly depressive course, and particularly in patients' nonadherence with prescribed medications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".