Comparing Migraine Headache Index versus Monthly Migraine Days after Headache Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Nerve deactivation surgery for the treatment of migraine has evolved rapidly over the past 2 decades. Studies typically report changes in migraine frequency (attacks/month), attack duration, attack intensity, and their composite score-the Migraine Headache Index-as primary outcomes. However, the neurology literature predominantly reports migraine prophylaxis outcomes as change in monthly migraine days (MMD). The goal of this study was to foster common communication between plastic surgeons and neurologists by assessing the effect of nerve deactivation surgery on MMD and motivating future studies to include MMD in their reported outcomes. METHODS: An updated literature search was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The National Library of Medicine (PubMed), Scopus, and Embase were systematically searched for relevant articles. Data were extracted and analyzed from studies that met the inclusion criteria. RESULTS: A total of 19 studies were included. There was a significant overall reduction in MMDs [mean difference (MD), 14.11; 95% CI, 10.95 to 17.27; I 2 = 92%], total migraine attacks per month (MD, 8.65; 95% CI, 7.84 to 9.46; I 2 = 90%), Migraine Headache Index (MD, 76.59; 95% CI, 60.85 to 92.32; I 2 = 98%), migraine attack intensity (MD, 3.84; 95% CI, 3.35 to 4.33; I 2 = 98%), and migraine attack duration (MD, 11.80; 95% CI, 6.44 to 17.16; I 2 = 99%) at follow-up (range, 6 to 38 months). CONCLUSION: This study demonstrates the efficacy of nerve deactivation surgery on the outcomes used in both the plastic and reconstructive surgery and neurology literature.
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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 | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.050 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".