60. A Comparison of Outcomes in Trigger Site Deactivation Surgery: A Systematic Review and Meta-analysis of the Migraine Headache Index versus Monthly Migraine Days
Bibliographic record
Abstract
PURPOSE: To foster common communication between plastic surgeons and neurologists alike by assessing the effect of trigger site deactivation surgery on monthly migraine days (MMD), the preferred outcome measure of neurologists, compared to outcomes traditionally reported in the plastic surgery literature. METHODS: An updated literature search was performed according to the PRISMA guidelines. The National Library of Medicine (PubMed), Scopus, and EMBASE were systematically searched for relevant articles. Data was extracted and analyzed from studies which met the inclusion criteria. RESULTS: A total of 19 studies were included in this review. There was a significant overall reduction in monthly migraine days (mean difference [MD] 14.11, 95% CI 10.95 to 17.27; I2 = 92%), migraine headache index (MD 76.59, 95% CI 60.85 to 92.32; I2 = 98%), migraine intensity (MD 3.84, 95% CI 3.35 to 4.33; I2 = 98%), and migraine duration (MD 11.80, 95% CI 6.44 to 17.16; I2 = 99%) at follow-up (range 6-38 months). CONCLUSION: This study demonstrates the efficacy of trigger site deactivation surgery on outcomes reported by both the plastic surgery and neurology communities. We hope this will open a common conversation and lead to optimized management for treatment-refractory migraine patients.
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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 | 0.015 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".