Speaker 4: Lakshmi Yatham, Canada
Bibliographic record
Abstract
Introduction: First-episode mania (FEM) is the first feasible diagnostic and therapeutic opportunity.Lithium is established as a first-line treatment for bipolar disorder.More recently antipsychotic drugs including quetiapine have been found to be effective in the treatment of mania, depression and in maintenance.However, the comparative benefits of these agents in the maintenance phase after a first episode of mania are unclear.This study compared the differential clinical effect of Lithium and Quetiapine after a first episode of mania.Method: The study was a single-blind, randomised controlled trial of 61 participants in remission from a first episode of severe mania.Participants were stabilised on the combination of lithium or quetiapine and were then randomised to either agent as maintenance treatment over a 12-month follow-up period.The groups were compared on performance outcomes using an extensive clinical battery including mood, functioning, psychotic and quality of life measures conducted at baseline, month 3 and month 12 follow-up time-points.Results: At endpoint, there was an advantage for lithium over quetiapine on measures of depression, psychosis and global impression.Conclusion: This study suggests that in a group of young individuals with a first episode of mania, lithium may have clinical advantages over quetiapine over the maintenance phase.This data is in contrast to some published reports suggesting broad equivalence, and raises questions about stage specific patterns of response as well as differential efficacy in classic severe mania.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.540 | 0.141 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".