Management of Migraine and the Accessibility of Specialist Care – Findings from a Multi-national Assessment of 28 Healthcare Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<bold>Introduction:</bold> Migraine is one of the leading reasons for patient access to neurology services. Waiting lists can limit patients’ ability to access specialist care, even at specialised headache centres. Our study aims to investigate this issue, identify possible root causes and also document existing good practices. <bold>Methods:</bold> We conducted a study in a sample of 28 headache centres and their networks in six countries by performing in-depth interviews with 166 healthcare professionals. <bold>Results:</bold> The waiting list for new patients and follow-up visits exceeded 3 months in 61% and 36% of centres, respectively. Patients waited on average 6 months for their first consultation, with peaks beyond 12 months. Five areas were identified as common root <bold>causes:</bold> (1) inappropriate referral of patients with low-frequency episodic migraine or patients under acute treatment, (2) lack of triage/priority allocation, (3) limited resource availability or resources dedicated to migraine, (4) limited delegation of activities, and (5) suboptimal management of follow-up visits. <bold>Conclusion:</bold> Our work highlights a gap between best practices for migraine management proposed in the literature and current real-world practice. Guidelines recommend a network approach to bridge different levels of care. Based on our findings, consistency in practice amongst specialised headache clinics and integration with primary care represent an important area for further improvement.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it