Treatment patterns and decision drivers to discharge patients with depression hospitalised for acute suicidal ideation in Europe
Bibliographic record
Abstract
BACKGROUND: There is limited published information about the management of patients with major depressive disorder (MDD) hospitalised for acute suicidal ideation (SI). This study aimed to identify treatment patterns and unmet needs in the management of these patients and the decision drivers for hospital discharge. METHODS: Cross-sectional survey-based study enrolling hospital-based European psychiatrists. The study had a qualitative and a quantitative stage, including a conjoint exercise. RESULTS: Each respondent (N = 413) managed, on average, 62 MDD patients with acute SI per typical three-month period; 76% of these patients required hospitalisation. Severity of SI and severity of MDD were considered the most important factors for hospital admission and discharge. In the conjoint analysis, these attributes accounted for 54% of the discharge decision. Key treatment goals included improving depressive symptoms and achieving MDD remission. Antidepressants were a standard treatment for 98% of respondents but 63% defined rapid onset of action as a critical unmet need, followed by a good tolerability profile (34%). LIMITATIONS: The study has a cross-sectional design representing respondents' behaviour and attitudes at a particular point in time. In the conjoint analysis, the results represent stated behaviour and not observed clinical behaviour. CONCLUSIONS: Physicians' decisions to admit and discharge patients with MDD hospitalised for acute SI are mostly driven by the severity of SI and depression. Antidepressants with rapid onset of action, which can quickly improve depressive symptoms, represent a key unmet need for these patients and may contribute to a higher likelihood of early discharge.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".