Improving the detection of chronic migraine: Development and validation of Identify Chronic Migraine (ID-CM)
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, particularly chronic migraine (CM), is underdiagnosed and undertreated worldwide. Our objective was to develop and validate a self-administered tool (ID-CM) to identify migraine and CM. METHODS: ID-CM was developed in four stages. (1) Expert clinicians suggested candidate items from existing instruments and experience (Delphi Panel method). (2) Candidate items were reviewed by people with CM during cognitive debriefing interviews. (3) Items were administered to a Web panel of people with severe headache to assess psychometric properties and refine ID-CM. (4) Classification accuracy was assessed using an ICHD-3β gold-standard clinician diagnosis. RESULTS: Stages 1 and 2 identified 20 items selected for psychometric validation in stage 3 (n = 1562). The 12 psychometrically robust items from stage 3 underwent validity testing in stage 4. A scoring algorithm applied to four symptom items (moderate/severe pain intensity, photophobia, phonophobia, nausea) accurately classified most migraine cases among 111 people (sensitivity = 83.5%, specificity = 88.5%). Augmenting this algorithm with eight items assessing headache frequency, disability, medication use, and planning disruption correctly classified most CM cases (sensitivity = 80.6%, specificity = 88.6%). DISCUSSION: ID-CM is a simple yet accurate tool that correctly classifies most individuals with migraine and CM. Further testing in other settings will also be valuable.
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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) | 0.000 | 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 | 0.000 |
| 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