Clinical recommendations for the care of people with treatment-refractory obsessive-compulsive disorder when undergoing deep brain stimulation
Bibliographic record
Abstract
Deep brain stimulation is an emerging therapy for treatment-refractory obsessive-compulsive disorder patients. Yet, accessibility is limited, treatment protocols are heterogeneous and there is no guideline or consensus on the best practices. Here, we combine evidence from scientific investigations, expert opinions and our clinical expertise to propose several clinical recommendations from the pre-operative, surgical and post-operative phases of deep brain stimulation care for treatment-refractory obsessive-compulsive disorder patients. A person-centered and biopsychosocial approach is adopted. Briefly, we discuss clinical characteristics associated with response, the use of improved educational materials, an evaluative consent process, comprehensive programming by an expert clinician, a more global assessment of treatment efficacy, multi-disciplinary adjunct psychotherapy and the importance of peer support programs. Furthermore, where gaps are identified, future research suggestions are made, including connectome surgical targeting, scientific evaluation of hardware models and health economic data. In addition, we encourage collaborative groups of data and knowledge sharing by way of a clinical registry and a peer group of programming clinicians. We aim to commence a discussion on the determinants of deep brain stimulation efficacy for treatment-refractory obsessive-compulsive disorder patients, a rare and severe patient group, and contribute to more standardized and evidence-based practices.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.013 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".