Posterior Hypothalamic Region Deep Brain Stimulation for the Treatment of Aggression Disorders in Patients with Intellectual Disability: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Aggressive disorders, in patients with intellectual disability, are satisfactorily managed with an educational, psychological, and pharmacological approach. Posterior hypothalamic region deep brain stimulation emerged in the last two decades as a promising treatment for patients with severe aggressive disorders. However, limited experiences are reported in the literature. METHODS: A systematic review was performed following PRISMA guidelines and recommendations by querying PubMed and Embase on August 24th, 2022, with the ensuing string parameters: ([deep brain stimulation] OR [DBS]) AND ([aggressiv*] OR disruptive). Cochrane Library, DynaMed, and ClinicalTrials.gov were consulted using the combination of keywords "deep brain stimulation" and "aggressive" or "aggression". The clinical outcome at the last follow-up and the rate of complications were considered primary and secondary outcomes of interest. RESULTS: The initial search identified 1,080 records, but only 10 studies met the inclusion criteria and were considered. The analysis of clinical outcome and complications was therefore performed on a total of 60 patients. Quality of all selected studies was classified as high, but one. Mean Overt Aggression Scale (OAS) improvement was 68%, while Inventory for Client Agency Planning (ICAP) improvement ranged between 38.3% and 80%. Complications occurred in 4 patients (6.7%). CONCLUSION: Posterior hypothalamic region deep brain stimulation may be considered a valuable option for patients with severe aggression disorders and ID. This review can represent a mainstay for those who will be engaged in the surgical treatment of these patients.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".