Effectiveness and safety of neuroablation for severe and treatment-resistant obsessive–compulsive disorder: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Several neuroablative procedures are available for severe and treatment-resistant obsessive-compulsive disorder (OCD), but limited knowledge about their relative clinical advantages and disadvantages poses obstacles for treatment decision-making. Methods: We searched PubMed, Embase, Scopus, Web of Knowledge and the Cochrane Library for reports up to February 2019. We reviewed the literature on the effectiveness (assessed using the Yale-Brown Obsessive Compulsive Scale [Y-BOCS]) and safety of various neuroablative interventions for severe and treatment-resistant OCD. Results: We included 23 studies involving 487 patients in the systematic review; 21 studies with 459 patients entered meta-analysis. Overall, neuroablation achieved a response rate (proportion of patients with ≥ 35% reduction in Y-BOCS) of 55%. Most of the adverse events (88.4%) were mild and transient. The top 3 adverse events were headache (14.9%), cognitive deficits (9.1%) and behaviour problems (8.1%). Severe or permanent adverse events included personality changes (2.3%) and brain edema or brain cyst (1.5%). The response rates associated with capsulotomy, limbic leucotomy and cingulotomy were 59% (95% confidence interval [CI] 54-65), 47% (95% CI 23-72) and 36% (95% CI 23-50), respectively. Interventions with different coverages of the dorsal part of the internal capsule were associated with different adverse-event profiles but were unlikely to modify clinical effectiveness. Limitations: The level of evidence of most included studies was relatively low. Conclusion: Ablative surgeries are safe and effective for a large proportion of patients with severe and treatment-resistant OCD. Among the available procedures, capsulotomy seemed to be the most effective. Further research is needed to improve clinical effectiveness and minimize risks.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".