Clinical Utility of an Instrument Assessing Migraine Disability: The Migraine Disability Assessment (MIDAS) Questionnaire
Bibliographic record
Abstract
OBJECTIVE: We evaluated the agreement between Migraine Disability Assessment (MIDAS) scores and independent physician judgments about pain, disability, and treatment needs based on patient medical histories. BACKGROUND: The MIDAS questionnaire measures headache-related disability as lost time due to headache from paid work or school, household work, and nonwork activities. METHODS: Twelve histories from patients with migraine were presented to 49 primary and specialty care physicians unaware of the MIDAS scores. Physicians graded each patient for pain level (mild, moderate, or severe), level of disability (none, mild, moderate, or severe), and need for medical care (from 0 [lowest] to 100 [highest]). Physicians also identified MIDAS scores they associated with different degrees of disability and with the urgency to prescribe an effective treatment during the first consultation. RESULTS: The physicians' perceptions of the need for medical care based on medical histories correlated with the MIDAS score (r =.69). Estimates of pain and disability by physicians were directly correlated with increasing MIDAS scores. Using the physicians' clinical judgments, the overall MIDAS score was categorized into four grades of increasing severity. CONCLUSIONS: Scores on the MIDAS are highly correlated with physician judgments regarding patients' pain, disability, and need for medical care. These findings support the potential utility of the MIDAS questionnaire in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".