HIT‐6 and MIDAS as Measures of Headache Disability in a Headache Referral Population
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to compare the headache impact test (HIT-6) and the migraine disability assessment scale (MIDAS) as clinical measures of headache-related disability. BACKGROUND: The degree of headache-related disability is an important factor in treatment planning. Many quality of life and headache disability measures exist but it is unclear which of the available disability measures is the most helpful in planning and measuring headache management. METHODS: We compared HIT-6 and MIDAS scores from 798 patients from the Canadian Headache Outpatient Registry and Database (CHORD). Correlation and regression analyses were used to examine the relationships between the HIT-6 and MIDAS total scores, headache frequency and intensity, and Beck Depression Inventory (BDI-II) scores. RESULTS: A positive correlation was found between HIT-6 and MIDAS scores (r = 0.52). The BDI-II scores correlated equally with the HIT-6 and the MIDAS (r = 0.42). There was a non-monotonic relationship between headache frequency and the MIDAS, and a non-linear monotonic relationship between headache frequency and the HIT-6 (r = 0.24). The correlation was higher between the intensity and the HIT-6 scores (r = 0.46), than MIDAS (r = 0.26) scores. Seventy-nine percent of patients fell into the most severe HIT-6 disability category, compared with the 57% of patients that fell into the most severe MIDAS disability category. Significantly more patients were placed in a more severe category with the HIT-6 than with the MIDAS (McNemar chi-square = 191 on 6 d.f., P < .0001). CONCLUSIONS: The HIT-6 and MIDAS appear to measure headache-related disability in a similar fashion. However, some important differences may exist. Headache intensity appears to influence HIT-6 score more than the MIDAS, whereas the MIDAS was influenced more by headache frequency. Using the HIT-6 and MIDAS together may give a more accurate assessment of a patient's headache-related disability.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".