Pharmacological Treatment of Disinhibition in Acquired Brain Injury
Bibliographic record
Abstract
PURPOSE/BACKGROUND: Traumatic brain injury is a major universal public health concern and results in chronic neurobehavioral sequelae including disinhibition. Objectives of this study were to review the literature on pharmacological treatment of disinhibition post-acquired brain injury (ABI), describe a snapshot of pharmacotherapy used in ABI at a tertiary neuropsychiatric unit in British Columbia, Canada, and share expert opinion. METHODS/PROCEDURES: A retrospective chart review of 11 patients from October to December 2021 was conducted based on exclusion criteria: age greater than 18 years, primary neurodegenerative conditions, or aphasia. Patient demographics, behavioral and cognitive test results, and disinhibition treatment were recorded. A brief review of the literature was conducted to find the best available evidence of pharmacological interventions to treat disinhibition post-ABI. FINDINGS/RESULTS: In ABI, there was a high utilization of antipsychotics and benzodiazepines, at 91% and 64% respectively, in patients with severe cognitive deficit and disinhibition. Mood stabilizers and nonselective β-blockers were less prescribed in this population at 73% and 18%. At the point of data collection, all the patients had responded well to treatment and were in the maintenance phase of their pharmacological treatment. IMPLICATIONS/CONCLUSIONS: A limited number of studies with weak methodology suggest that mood stabilizers and β-blockers should be first line for disinhibition treatment. Our findings are complementary to the literature describing treatment of severe disinhibition. The choice of treatment for disinhibition depends on factors including nature and severity of target symptoms, level of drug evidence, patient-tailored objectives, concurrent psychiatric diagnoses, clinical experience of clinicians, adverse drug reactions, and treatment acuity.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".