Diagnosing migraine in research and clinical settings: The validation of the Structured Migraine Interview (SMI)
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
BACKGROUND: Migraine is a common disorder that is highly co-morbid with psychopathological conditions such as depression and anxiety. Despite the extensive research and availability of treatment, migraine remains under-recognised and undertreated. The aim of this study was to design a short and practical screening tool to identify migraine for clinical and research purposes. METHODS: The structured migraine interview (SMI) based on the International Classification of Headache Disorders (ICHD) criteria was used in a clinical setting of headache sufferers and compared to clinical diagnosis by headache specialist. In addition to the validating characteristics of the interview different methods of administration were also tested. RESULTS: The SMI has high sensitivity (0.87) and modest specificity (0.58) when compared to headache specialist's clinical diagnosis. CONCLUSIONS: Our study demonstrated that a structured interview based on the ICHD criteria is a useful and valid tool to identify migraine in research settings and to a limited extent in clinical settings, and could be used in studies on large samples where clinical interviews are less practical.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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 it