Ablation Surgeries for Treatment-Resistant Depression: A Meta-Analysis and Systematic Review of Reported Case Series
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Ablative lesion procedures remain as the last option in treatment of refractory depression. Contemporary ablative psychosurgeries involve producing lesions in the anterior limb of the internal capsule (bilateral anterior capsulotomy - BAC), the supragenual anterior cingulate gyrus and cingulum (bilateral anterior cingulotomy - BACING), and subgenual anterior cingulate gyrus and subcortical orbitofrontal white matter (bilateral subcaudate tractotomy - BST). A combination of BACING and BST is known as limbic leukotomy (bilateral limbic leukotomy - BLL). All procedures claim some success, but cohorts are small, depression assessment instruments differ, and inclusion and outcome criteria and follow-up duration vary. In some cohorts, more than one type of surgery was performed in several patients, further confounding interpreting the available data. Current evidence is equivocal on which surgical target works best. Method and Aim: This systematic review and meta-analysis using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) standard on published cohorts was conducted to review and identify which is the best standalone ablative procedure for treatment-resistant depression (TRD) based on response rate (event rate) and adverse-effect profile using the Comprehensive Meta-Analysis software. RESULTS AND CONCLUSION: As a standalone neurosurgical procedure, we found that BAC appears to be the most effective and safest of all the ablative targets for TRD. A major limitation of this conclusion is the paucity of published case series where sample sizes are small and all are open label.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".