Valproic acid for agitation in the intensive care unit: an observational study of psychiatric consults
Bibliographic record
Abstract
Abstract Background Agitation is a common clinical problem encountered in the intensive care unit (ICU). Treatment options are based on clinical experience and sparse quality literature. Aim The aim of this study was to evaluate the effect of valproic acid (VPA) as adjuvant treatment for agitation in the ICU as well as to identify independent predictors of response. Method This retrospective single center observational study evaluated adult patients admitted to the ICU for whom a psychiatric consultation was requested for agitation management, with agitation defined as a Richmond Agitation Sedation Score of 2 or greater. A descriptive analysis of the proportion of agitation-free patients per day of follow-up, the incidence of agitation-related-events, as well as the evolution of co-medications use over time are presented. A logistic regression model was used to assess predictors of VPA response, defined as being agitation-free on Day 7 and GEE models were used to evaluate the independent effect of VPA as adjuvant therapy for agitation in the critically ill. Results One hundred seventy-five (175) patients were included in the study with 78 receiving VPA. The percentage of agitation-free patients was 6.5% (5/77) on Day 1, 14.1% (11/78) on Day 3 and 39.5% (30/76) on Day 7. Multivariate regression model for clinical and demographic variables identified female gender as predictor of response on Day 7 (OR 6.10 [1.18–31.64], p = 0.03). The independent effect of VPA was non-significant when compared to a control group. Conclusion Although VPA used as adjuvant treatment was associated with a decrease in agitation, its effect when compared to a control group did not yield significant results.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".