Deep Brain Stimulation as Clinical Innovation
Bibliographic record
Abstract
UNLABELLED: Deep brain stimulation (DBS) for psychiatric disorders needs to be investigated in proper research trials. However, there are rare circumstances in which DBS could be offered to psychiatric patients as a form of surgical innovation, therefore potentially blurring the lines between these research trials and health care. In this article, we discuss the conditions under which surgical innovation may be accepted as a practice falling at the frontiers of standard clinical care and research per se. However, recognizing this distinction does not settle all ethical issues. Our article offers ethical guideposts to allow clinicians, surgical teams, institutions, and institutional review boards to deliberate about some of the fundamental issues that should be considered before surgical innovation with psychiatric DBS is undertaken. We provide key guiding questions to sustain this deliberation. Then we review the normative and empirical literature that exists to guide reflection about the ethics of surgical innovation and psychiatric DBS with respect to general ethical questions pertinent to psychiatric DBS, multidisciplinary team perspectives in psychiatric DBS, mechanisms for oversight in psychiatric DBS, and capacity and consent in psychiatric DBS. The considerations presented here are to recognize the very specific nature of surgical innovation and to ensure that surgical innovation in the context of psychiatric DBS remains a limited, special category of activity that does not replace appropriate surgical research or become the standard of care based on limited evidence. ABBREVIATIONS: DBS, deep brain stimulationIRB, institutional review boardOCD, obsessive-compulsive disorder.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".