Lesional psychiatric neurosurgery: meta-analysis of clinical outcomes using a transdiagnostic approach
Bibliographic record
Abstract
BACKGROUND: Four ablative neurosurgical procedures are used in the treatment of refractory psychiatric illness. The long-term effects of these procedures on psychiatric symptoms across disorders has never been synthesised and meta-analysed. METHODS: A preregistered systematic review was performed on studies reporting clinical results following ablative psychiatric neurosurgery. Four possible outcome measures were extracted for each study: depression, obsessive-compulsive symptoms, anxiety and clinical global impression. Effect sizes were calculated using Hedge's g. Equipercentile linking was used to convert symptom scores to a common metric. The main outcome measures were the magnitude of improvement in depression, obsessive compulsive symptoms, anxiety and clinical global impression. The secondary outcome was a subgroup analysis comparing the magnitude of symptom changes between the four procedures. RESULTS: Of 943 articles, 43 studies reporting data from 1414 unique patients, were included for pooled effects estimates with a random-effects meta-analysis. Results showed that there was a large effect size for improvements in depression (g=1.27; p<0.0001), obsessive-compulsive symptoms (g=2.25; p<0.0001) and anxiety (g=1.76; p<0.0001). The pooled clinical global impression improvement score was 2.36 (p<0.0001). On subgroup analysis, there was only a significant degree of heterogeneity in effect sizes between procedure types for anxiety symptoms, with capsulotomy resulting in a greater reduction in anxiety than cingulotomy. CONCLUSIONS: Contemporary ablative neurosurgical procedures were significantly associated with improvements in depression, obsessive-compulsive symptoms, anxiety and clinical global impression. PROSPERO REGISTRATION NUMBER: CRD42020164784.
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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 | 0.025 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.081 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.003 |
| 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".