DBS for Aggressiveness, Results in a Series of 33 Patients
Bibliographic record
Abstract
Introduction: Medical literature consistently identifies the sensorimotor Pathological refractory aggression poses a major clinical challenge. Despite advances in pharmacological and psychotherapeutic interventions, a significant proportion of patients continue to exhibit severe aggressive behavior, often with detrimental effects on their quality of life and social functioning. In this context, deep brain stimulation (DBS) has emerged as a potential therapeutic alternative for individuals who do not respond to conventional treatments.Method: We present a series of 33 patients with pharmacoresistant aggression who underwent DBS surgery at our institutions. All patients had previously failed multiple lines of pharmacological and behavioral therapy. The indication for DBS was based on the persistence of severe aggressive behavior that posed a risk to the patient or others, and on the lack of sustained response to standard treatments.Discussion: More than 90% of the patients exhibited a significant reduction in aggressive behavior following DBS implantation. Additionally, improvements in sleep and eating patterns were frequently observed. These findings suggest that DBS may be effective not only in managing treatment-resistant aggression but also in alleviating comorbid symptoms that contribute to overall patient dysfunction. Optimal outcomes likely depend on careful target selection, individualized programming, and multidisciplinary follow-up.Conclusions: Deep brain stimulation appears to be a promising therapeutic option for patients with refractory pathological aggression. The high response rate observed in this case series supports its consideration in complex clinical scenarios. Further controlled studies are needed to evaluate long-term efficacy and safety.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".