Pharmacological Interventions for Agitated Behaviors in Patients with Traumatic Brain Injury: An Updated Systematic Review
Bibliographic record
Abstract
The aim of this updated systematic review was to assess the efficacy and safety of pharmacological agents in the management of agitated behaviors following traumatic brain injury (TBI). We updated a 2019 systematic review, which originally included 21 studies, by performing a search strategy in MedLine, Embase, PsychInfo, Cinhal, Directory of Open Access Journals, and Latin American and Caribbean Literature on Health Sciences Literature (up to Jan 7 th , 2025) for evidence on the risks and benefits of nine medication classes used to control agitated behaviors following TBI. We included all randomized controlled trials, quasi-experimental and observational studies examining the effects of medications administered to control agitated behaviors in TBI patients. Of the 58 studies screened in full-text, 11 additional studies were added to the 21 original studies for a total of 32 studies. Of these new studies, three studies evaluating dexmedetomidine suggested some potential benefits in reducing agitation. New studies on risperidone, olanzapine, carbamazepine, and valproic acid failed to show efficacy compared with control groups. Among studies identified in the first review, propranolol did reduce intensity of agitation but not its frequency. In conclusion, there remain insufficient data to recommend the use of any medications for the management of agitation following TBI. Dexmedetomidine may have potential benefit in an acute setting, and the benefits of antipsychotics, carbamazepine, and amantadine remain unclear. Beta-blockers and valproic have shown benefits, but results are inconsistent. More studies in the acute, rehabilitation, and outpatient settings are needed to assess the efficacy and safety of pharmacological agents for the management of agitated behaviors.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".