Course and Outcome after the First Manic Episode in Patients with Bipolar Disorder: Prospective 12-Month Data from the Systematic Treatment Optimization Program for Early Mania Project
Bibliographic record
Abstract
OBJECTIVE: To describe clinical characteristics, course, and outcome during a 1-year period after the first manic episode in patients with bipolar disorder (BD). METHODS: This paper describes the project design, demographics, clinical outcomes, and predictors at 6 months to 1 year of follow-up of the first 53 recruited subjects with first-episode mania from the Systematic Treatment Optimization Program for Early Mania. RESULTS: Survival analysis for recurrence of mood episodes showed that 46.7% of patients survived without a mood episode during 1-year of follow-up, and the mean time-to-mood event was 7.9 months. Earlier age of onset was the only variable that significantly predicted recurrence of mood episodes. When examined separately, the survival rates were 76% for a manic episode and 58.7% for a depressive episode. CONCLUSION: These results suggest that recurrences are common after the first manic episode with more than one-half of the patients experiencing a mood event within 12 months. Aggressive treatment strategies aimed at preventing depressive episodes are needed in the management of early course BD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".