Increasing Collaboration between Headache Medicine and Plastic Surgery in the Surgical Management of Chronic Headache
Bibliographic record
Abstract
Introduction: Chronic headache is one of the most disabling conditions afflicting humankind. The management of chronic headaches has, to date, been only partially successful. The goal of this paper is to highlight the importance of collaboration between surgeons and headache physicians in treating this condition. Methods: We present a narrative review of migraine pathophysiology, its medical and surgical treatment options, and the important role of collaboration between headache physicians and surgeons. Results: Migraine headaches can be treated with both medication-based regimens and surgery. Novel medications such monoclonal antibodies directed at the CGRP molecule or its receptor have recently been FDA approved as an effective treatment modality in chronic migraines. However, these medications are associated with a high cost, and there is a paucity in data regarding effectiveness compared to other treatment modalities. The pathophysiology of headache likely exists along a spectrum with peripheral - extracranial and meningeal - factors at one end and central - brain - factors at the other, with anatomic and physiologic connections between both ends. Recent evidence has clearly shown that surgical decompression of extracranial nerves improves headache outcomes. However, appropriate patient selection and preoperative diagnosis are of paramount importance to achieve excellent outcomes. Conclusions: Surgeons and headache physicians who are interested in providing treatment for patients with chronic headache should strive to form a close collaboration with each other in order to provide the optimal plan for migraine/headache 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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".