Psychiatric Neurosurgery: A Survey on the Perceptions of Psychiatrists and Residents
Bibliographic record
Abstract
OBJECTIVES: To evaluate the attitudes and perceptions of psychiatrists and psychiatry residents regarding neurosurgical procedures for treating psychiatric disorders and to identify potential barriers to patient referral. METHODS: A survey consisting of 25 questions was created using SurveyMonkey and was distributed to psychiatrists and psychiatry residents in Quebec. The study was approved by the McGill University Health Center's Research Ethics Board. Descriptive statistics and Friedman's test were performed using SPSS software. RESULTS: A total of 99 participants, including 64 residents and 35 psychiatrists, completed more than 75% of the survey and were included in data analysis. Overall, participants were significantly (p < 0.0005) more comfortable in referring patients suffering from treatment-resistant obsessive-compulsive disorder than from treatment-resistant major depressive disorder and preferred to refer patients for deep brain stimulation (DBS) rather than for anterior cingulotomy/capsulotomy (AC). Only 11.43% of psychiatrists had ever referred a patient for AC or DBS, and 34.69% of respondents felt that these procedures were dangerous. Lack of knowledge (82.83%) was viewed as the principal limiting factor, and 57.58% of respondents identified ≥6 different barriers to patient referral. The majority of participants (69.39%) were interested in improving their knowledge on psychiatric neurosurgery, and 82.65% felt that this subject should be included in the psychiatry residency curriculum. CONCLUSION: Overall, participants acknowledged having many limitations to referring patients for neurosurgical interventions. While informative conferences discussing neuromodulation/neuroablation could easily address many barriers, further studies are required to assess how these could change attitudes and patterns of referral.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".