The Transition of Deep Brain Stimulation from Disease Specific to Symptom Specific Indications
Bibliographic record
Abstract
The success of chronic deep brain stimulation (DBS) and electrical neuro-network modulation (ENM) to address neurological and neuropsychiatric disorders has led the Food and Drug Administration (FDA), and also other worldwide regulatory agencies to grant approval for the use of DBS in specific disorders. In the United States, DBS is FDA approved for the treatment of advanced Parkinson's disease (PD), essential tremor (ET), obsessive compulsive disorder (OCD), and for dystonia. OCD and dystonia have been approved under a mechanism referred to as a humanitarian device exemption (HDE). However, as the field of DBS and ENM evolve there has been a shift in practice patterns from targeting diseases to targeting specific and disabling symptoms. This shift has been driving interdisciplinary DBS boards to collect, and to address symptom profiles in all potential DBS candidates. Based on a specific symptom profile, a strategic and personalized medicine approach can be undertaken. The personalized approach will take into consideration the brain target, a unilateral versus a bilateral procedure, and the potential for use of more than one DBS lead per brain hemisphere. Additionally, a personalized approach to DBS will also facilitate improved pre-operative medication adjustments, as well as optimal post-operative medication, behavioral, and device management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".