The role of different patterns of psychomotor symptoms in major depressive episode: Pooled analysis of the BRIDGE and BRIDGE‐II‐MIX cohorts
Bibliographic record
Abstract
BACKGROUND: Psychomotor agitation (PA) or retardation (PR) during major depressive episodes (MDEs) have been associated with depression severity in terms of treatment-resistance and course of illness. OBJECTIVES: We investigated the possible association of psychomotor symptoms (PMSs) during a MDE with clinical features belonging to the bipolar spectrum. METHODS: The initial sample of 7689 MDE patients was divided into three subgroups based on the presence of PR, PA and non-psychomotor symptom (NPS). Univariate comparisons and multivariate logistic regression models were performed between subgroups. RESULTS: A total of 3720 patients presented PR (48%), 1971 showed PA (26%) and 1998 had NPS (26%). In the PR and PA subgroups, the clinical characteristics related to bipolarity, along with the diagnosis of bipolar disorder (BD), were significantly more frequent than in the NPS subgroup. When comparing PA and PR patients, the former presented higher rates of bipolar spectrum features, such as family history of BD (OR = 1.39, CI = 1.20-1.61), manic/hypomanic switches with antidepressants (OR = 1.28, CI = 1.11-1.48), early onset of first MDE (OR = 1.40, CI = 1.26-1.57), atypical (OR = 1.23, CI = 1.07-1.42) and psychotic features (OR = 2.08, CI = 1.78-2.44), treatment with mood-stabilizers (OR = 1.39, CI = 1.24-1.55), as well as a BD diagnosis according to both the DSM-IV criteria and the bipolar specifier criteria. When logistic regression model was performed, the clinical features that significantly differentiated PA from PR were early onset of first MDE, atypical and psychotic features, treatment with mood-stabilizers and a BD diagnosis according to the bipolar specifier criteria. CONCLUSIONS: Psychomotor symptoms could be considered as markers of bipolarity, illness severity, and treatment complexity, particularly if PA is present.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".