Retrospective Observational Study of Daytime Add-On Administration of Zopiclone to Difficult-to-Treat Psychiatric Inpatients With Unpredictable Aggressive Behavior, With or Without EEG Dysrhythmia
Bibliographic record
Abstract
Managing violent behavior is a particularly challenging aspect of hospital psychiatric care. Available pharmacological interventions are often unsatisfactory. Aim: To assess the effectiveness and safety of daytime zopiclone add-on administration in violent and difficult-to-treat psychiatric inpatients. Methods: Chart review of inpatients treated with daytime zopiclone, between 2014 and 2018, with up to 12 weeks follow-up. Effectiveness was retrospectively assessed with the Clinical Global Impression rating scale (CGI) and the frequency and severity of aggressive incidents recorded with the Staff Observation Aggression Scale-Revised (SOAS-R). Results: Forty-five (30 male, 15 female) cases, 18–69 years age range, average (SD) baseline CGI-S score of 5.4 (1.0), and a variety of diagnoses. Sixty-nine percent showed CGI-S improvement of any degree. For patients with at least one aggressive incident within 7 days prior to initiation of zopiclone (N = 22), average (SD) SOAS-R-Severity LOCF to baseline change was −3.5 (2.7) P < 0.0001. Most patients reported no side effects; 24% reported one or more side effects, and 11% discontinued zopiclone due to sedation (4), insomnia (1) or slurred speech (1). No SAEs were recorded. Zopiclone maximum daily dose correlated with CGI-S baseline-to-LOCF change (rho = −0.5, P = 0.0003). The ROC AUC of zopiclone maximum daily dose and improvement on CGI-S was 0.84 (95% CI 0.70–0.93, P < 0.0001). The ROC AUC of zopiclone maximum daily dose and SOAS-R-N improvement was 0.80 (95% CI 0.58–0.92; P = 0.0008) and maximum Youden's index value was achieved at a dose of >30 mg. Conclusions: Zopiclone doses >30 mg daily achieved the best anti-aggressive effect.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".