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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

2023· review· en· W4366773937 on OpenAlexaff
Benjamin Ormseth, Hassan ElHawary, María T. Huayllani, Jeffrey E. Janis

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2023
Typereview
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMigraineMedicineNeurologyMeta-analysisScopusWeb of scienceMEDLINEAnesthesiaSurgeryPhysical therapyInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.555
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0290.006
Bibliometrics0.0010.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.209
GPT teacher head0.410
Teacher spread0.201 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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