C.01 Endoscopic versus open microvascular decompression of trigeminal neuralgia: a systematic review and comparative meta-analysis
Bibliographic record
Abstract
Background: Microvascular decompression (MVD) is commonly used in the treatment of trigeminal neuralgia with positive clinical outcomes. Fully endoscopic microvascular decompression (E-MVD) has been proposed as a minimally invasive, effective alternative, but a comparative review of the two approaches in the literature has not been conducted. Methods: We performed a meta-analysis comparing patient outcome rates and complications for both techniques. From a pool of 1,039 studies, 22 articles were selected for review: 12 open MVD and 10 E-MVD. The total number of patients was 6,734. Results: Good pain relief was achieved in 81% of MVD and 88% of E-MVD patients, with a mean recurrence rate of 14% and 9% respectively. Average rates of complications in MVD versus E-MVD included facial paresis or weakness, 9%, 3%; -hearing loss, 4%, 1%; cerebrospinal leak, both 3%; cerebellar damage and infection, 2%, <1%; and mortality <1%, 0% respectively. Conclusions: The reviewed literature revealed similar clinical outcomes with respect to pain relief for both approaches. Recurrence rate and incidence of complications, notably facial paresis and hearing loss were higher for MVD. We concluded that E-MVD appears to offer at least as good a surgical outcome as MVD, with possibly a shorter operative time, smaller craniectomy and lower recurrence rates.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.030 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".