Assessment and management of agitation in psychiatry: Expert consensus
Bibliographic record
Abstract
BACKGROUND: Psychomotor agitation is associated with different psychiatric conditions and represents an important issue in psychiatry. Current recommendations on agitation in psychiatry are not univocal. Actually, an improper assessment and management may result in unnecessary coercive or sedative treatments. A thorough and balanced review plus an expert consensus can guide assessment and treatment decisions. METHODS: An expert task force iteratively developed consensus using the Delphi method. Initial survey items were based on systematic review of the literature. Subsequent surveys included new, re-worded or re-rated items. RESULTS: Out of 2175 papers assessing psychomotor agitation, 124 were included in the review. Each component was assigned a level of evidence. Integrating the evidence and the experience of the task force members, a consensus was reached on 22 statements on this topic. CONCLUSIONS: Recommendations on the assessment of agitation emphasise the importance of identifying any possible medical cause. For its management, experts agreed in considering verbal de-escalation and environmental modification techniques as first choice, considering physical restraint as a last resort strategy. Regarding pharmacological treatment, the "ideal" medication should calm without over-sedate. Generally, oral or inhaled formulations should be preferred over i.m. routes in mildly agitated patients. Intravenous treatments should be avoided.
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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 | 0.118 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".