Migraine Frequency and Intensity: Relationship With Disability and Psychological Factors
Bibliographic record
Abstract
BACKGROUND: Migraine can be disabling, but it varies greatly in frequency and intensity between individuals. It is not clear which clinical features have the greatest impact on a migraineur's quality of life. OBJECTIVE: To determine the influence of headache intensity and frequency on headache-related disability. METHODS: Patients who were referred to a headache clinic and given a diagnosis of migraine with or without aura or transformed migraine (n = 115) were divided into different groups based on headache frequency and mean headache intensity. Headache frequency was determined from patient diaries. Headache intensity also was assessed from patient diaries and from scores on the pain severity scale of the Multidimensional Pain Inventory (MPI). Headache-related disability was assessed with the Headache Disability Inventory and by scores on the activity interference scale of the MPI. The degree of depression present was assessed with the Beck Depression Inventory, and emotional distress was measured by scores on the affective distress scale of the MPI. RESULTS: In our patient population, higher mean headache intensity levels were associated with higher levels of headache-related disability. Our results also suggested that increased headache intensity is associated with higher levels of depression and emotional distress, although this correlation was statistically significant in only 1 of 4 comparisons. Headache frequency did not correlate with disability, depression, or emotional distress. CONCLUSIONS: For a headache referral population, headache intensity appears to be a major determinant of headache-related disability, and it also correlates, to some extent, with the degree of depression and emotional distress present. Headache frequency was not clearly related to disability or psychological factors.
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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 | 0.000 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".