Risperidone plus lithium versus risperidone plus valproate in acute and continuation treatment of mania
Bibliographic record
Abstract
This exploratory analysis was performed to compare the efficacy and tolerability of risperidone when added to two different mood stabilizers (lithium or valproate) for mania in bipolar disorder. Patients receiving lithium or valproate at baseline were drawn from the database of a 12-week, open-label risperidone study. The primary efficacy measure was the Young Mania Rating Scale (YMRS). Other assessments included the Hamilton Rating Scale for Depression (HAM-D), Clinical Global Impression (CGI) of improvement, and safety measures. The analysis included 33 patients on lithium plus risperidone and 46 patients on valproate plus risperidone. Both subgroups had comparable baseline YMRS scores (lithium 28.2, valproate 28.7) and both had significant reductions in score by week 1 (P<0.0001). Comparable reductions in score continued for both subgroups until the end of the study (YMRS scores at week 12: lithium 4.6 and valproate 6.7). There were no significant differences in response rates (> or =50% improvement on YMRS) or remission rates (YMRS score < or = 8) between the two subgroups. At week 12, 88% of the lithium plus risperidone patients and 80% of the valproate plus risperidone patients were in remission. Similarly, HAM-D scores were significantly and comparably reduced in both subgroups, and improvement in CGI was the same. There was no difference between subgroups in the incidence of adverse events or weight gain. These data suggest that risperidone can be safely combined with either lithium or valproate, and that the efficacy is similar regardless of the mood stabilizer used.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".