Bibliographic record
Abstract
BACKGROUND: Chronic migraine is common, affecting approximately 1% of the general population, and causes significant disability. OBJECTIVE: To summarize optimal involvement of primary care physicians in chronic migraine care, and to provide algorithms to assist them in the diagnosis and management of patients with chronic migraine. METHODS: An analysis of diagnostic and treatment needs in chronic migraine, based on a synthesis of the medical literature and clinical experience. RESULTS: Chronic migraine represents the more severe end of the migraine spectrum, usually arises out of previous episodic migraine, and is characterized by headache on 15 days a month or more. Importantly, the headache needs to meet migraine diagnostic criteria on only 8 days a month in order to meet chronic migraine diagnostic criteria. When acute medication overuse is present, a second diagnosis of medication overuse headache should be made. If patients meet criteria for chronic migraine, this excludes a diagnosis of chronic tension-type headache. Acute therapy of chronic migraine is similar to episodic migraine, except that medication overuse is a much greater risk in chronic migraine and must be addressed. All patients should be considered for pharmacological prophylaxis, and the behavioral aspects of therapy should be emphasized. The two prophylactic drugs with the best evidence for efficacy in chronic migraine are topiramate and onabotulinumtoxinA. Given the disability caused by chronic migraine, these should both be available to patients as necessary. CONCLUSION: Management of chronic migraine is complex, and many patients are relatively refractory to therapy. Specialist referral will often be required and should not be unduly delayed. On the other hand, the primary care physician should be able to make the diagnosis, initiate therapy, and manage some less refractory patients without referral. The timing of referral should depend both on the expertise of the primary care physician in headache management and the patient's response to initial therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".