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 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 | 0.001 | 0.002 |
| 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".