Cognitive and Motor Function Effects of Antipsychotics in Traumatic Brain Injury: A Systematic Review of Pre-Clinical Studies
Bibliographic record
Abstract
Traumatic brain injury (TBI) survivors often suffer from agitated behaviors and will most likely receive pharmacological treatments. Choosing an optimal and safe treatment that will not interfere with neurological recovery remains controversial. By interfering with dopaminergic circuits, antipsychotics may impede processes important to cognitive recovery. Despite their frequent use, there have been no large randomized controlled studies of antipsychotics for the management of agitated behaviors during the acute TBI recovery period. We conducted a systematic review and meta-analysis of pre-clinical studies evaluating the effects of antipsychotics post-TBI on both cognitive and motor recovery. MEDLINE and Embase databases were searched up to August 2, 2023. Pre-clinical studies evaluating the effects of antipsychotics on cognitive and motor functions post-TBI were considered. Risk of bias was evaluated with the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool. We identified 15 studies including a total of 1188 rodents, mostly conducted in male Sprague-Dawley rats using cortical impact injury. The analysis revealed no consistent effect of haloperidol on motor functions, but risperidone was associated with a significant impairment in motor function on day 5 post-injury (7.05 sec; 95% confidence interval [CI]: 1.47, 12.62; I 2 = 92%). Other atypical antipsychotics did not result in impaired motor function. When evaluating cognitive function, haloperidol- (23.00 sec; 95% CI: 17.42–28.59; I 2 = 7%) and risperidone-treated rats (24.27 sec; 95% CI: 16.18–32.36; I 2 = 0%) were consistently impaired when compared to controls. In studies evaluating atypical antipsychotics, no impairments were observed. Clinicians should avoid the regular use of haloperidol and risperidone, and future human studies should be conducted with atypical antipsychotics.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".