Influence of sub-syndromal symptoms after remission from manic or mixed episodes
Bibliographic record
Abstract
BACKGROUND: Sub-syndromal symptoms in bipolar disorder impair functioning and diminish quality of life. AIMS: To examine factors associated with time spent with sub-syndromal symptoms and to characterise how these symptoms influence outcomes. METHOD: In a double-blind randomised maintenance trial, patients received either olanzapine or lithium monotherapy for 1 year. Stepwise logistic regression models were used to identify factors that were significant predictors of percentage time spent with sub-syndromal symptoms. The presence of sub-syndromal symptoms during the first 8 weeks was examined as a predictor of subsequent relapse. RESULTS: Presence of sub-syndromal depressive symptoms during the first 8 weeks significantly increased the likelihood of depressive relapse (relative risk 4.67, P<0.001). Patients with psychotic features and those with a greater number of previous depressive episodes were more likely to experience sub-syndromal depressive symptoms (RR=2.51, P<0.001 and RR=2.35, P=0.03 respectively). CONCLUSIONS: These findings help to identify patients at increased risk of affective relapse and suggest that appropriate therapeutic interventions should be considered even when syndromal-level symptoms are absent.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".