The Contemporary Practice of Psychiatric Surgery: Results from a Survey of North American Functional Neurosurgeons
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) for psychiatric indications is becoming increasingly safe and effective. As a result, the treatment of these conditions by neurosurgeons is becoming more widespread and a larger part of the functional neurosurgeons' practice. Given the troubled history of the field and its current renaissance, it is important to evaluate current practices, to serve as a baseline for future comparison, and to gauge changing cultural attitudes towards invasive neuromodulation. OBJECTIVES: The purpose of this study was to obtain a snapshot of the practice of the contemporary functional neurosurgeon engaged in psychiatric surgery. Current practices as well as attitudes towards the direction of the field and its future were also investigated. METHODS: We designed an online survey and distributed it electronically to 299 functional neurosurgeons in North America identified by membership in the World Society for Stereotactic and Functional Neurosugery. Subsequent statistical and thematic analysis was performed on the data obtained. RESULTS: Of 299 surveys sent out, 84 were completed (28%). Fifty percent of functional neurosurgeons are currently engaged in some form of psychiatric surgery, with DBS for obsessive-compulsive disorder and depression accounting for most cases. Most surgeons see psychiatric surgery as a growing field, with multidisciplinary teams and a greater engagement with psychiatry necessary for the expansion of the field. Opinions differed substantially on hypothetical scenarios dealing with surgical cognitive enhancement, with some in favour of it, citing patient autonomy and choice, and others drawing a clear distinction between pathologic and non-pathologic states. CONCLUSIONS: Surgery for psychiatric indications is a growing field in the contemporary functional neurosurgeon's practice. Most neurosurgeons see the field continuing to grow and psychiatric surgery becoming a larger part of their daily practice, with the appropriate ethical and psychiatric oversight. Future editions of this survey and their results can be used to trace the development and growth of the field as technology and cultural attitudes continue to evolve.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".