The History and Future of Ablative Neurosurgery for Major Depressive Disorder
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: There is an urgent need to develop safe and effective treatments for patients with treatment-resistant depression (TRD). Several neurosurgical procedures have been developed to treat the dysfunctional brain circuits implicated in major depression. OBJECTIVES: This review describes the most common ablative procedures used to treat major depressive disorder: anterior cingulotomy, subcaudate tractotomy, limbic leucotomy, and anterior capsulotomy. The efficacy and safety of each are discussed and compared with other current and emerging modalities, including deep brain stimulation (DBS) and MR-guided focused ultrasound (MRgFUS). METHODS: The PubMed and MEDLINE electronic databases were used in this study, through July 2016. Keywords, including "treatment resistant depression," and "ablative neurosurgery," etc. were used to generate reference hits. RESULTS: Approximately a third to half of patients who underwent ablative procedures achieved a treatment response and/or remission. The efficacy and safety profiles corresponding to both ablative procedures and DBS were very similar. CONCLUSIONS: The longitudinal experience with ablative procedures shows that there remains an important role for accurate, discrete lesions in disrupting affective circuitry in the treatment of TRD. New modalities, such as MRgFUS, have the potential to further improve the accuracy of ablative procedures, while enhancing safety by obviating the need for open brain surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it