Migraine care practices in primary care: results from a national US survey
Bibliographic record
Abstract
BACKGROUND: Primary care clinicians play a critical role in diagnosis and treatment of migraine, yet barriers exist. This national survey assessed barriers to diagnosis and treatment of migraine, preferred approaches to receiving migraine education, and familiarity with recent therapeutic innovations. METHODS: The survey was created by the American Academy of Family Physicians (AAFP) and Eli Lilly and Company and distributed to a national sample through the AAFP National Research Network and affiliated PBRNs from mid-April through the end of May 2021. Initial analyses were descriptive statistics, ANOVAs, and Chi-Square tests. Individual and multivariate models were completed for: adult patients seen in a week; respondent years since residency; and adult patients with migraine seen in a week. RESULTS: Respondents who saw fewer patients were more likely to indicate unclear patient histories were a barrier to diagnosing. Respondents who saw more patients with migraine were more likely to indicate the priority of other comorbidities and insufficient time were barriers to diagnosing. Respondents who had been out of residency longer were more likely to change a treatment plan due to attack impact, quality of life, and medication cost. Respondents who had been out of residency shorter were more likely to prefer to learn from migraine/headache research scientists and use paper headache diaries. CONCLUSIONS: Results demonstrate differences in familiarity with migraine diagnosis and treatment options based on patients seen and years since residency. To maximise appropriate diagnosis within primary care, targeted efforts to increase familiarity and decrease barriers to migraine care should be implemented.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".