Impact of migraine on patients and their families: the Migraine And Zolmitriptan Evaluation (MAZE) survey – Phase III
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of migraine on migraineurs and their families and evaluate migraineurs' preference for different treatment formulations. This study also assessed the prevalence and impact of migraine with menstruation. METHODS: Participants (n = 1028) from around the world (USA [39%], Canada [20%], Europe [37%] and other countries [4%]) completed an online questionnaire. Of these, 866 were migraineurs and 162 were non-migraineurs living with/related to migraineurs. Migraineurs were identified based on responses to a modified Kiel questionnaire and/or diagnosis of migraine by a doctor. Disability was quantified using the Migraine Disability Assessment Scale (MIDAS). RESULTS: Migraineurs missed more days from family/leisure activities than from work/school (mean 4.2 vs 2.4 days) in the previous 3 months. On an additional 6.2 days within the 3-month period, productivity at work/school was reduced by at least half. Inability and reduced ability (by at least half) to perform household work were reported on 6.0 and 6.5 days, respectively. Of the women surveyed, 51% identified menstruation as a trigger for attacks and 6% reported attacks solely with menstruation (i. e. attacks occurred during menstruation on at least 9 out of 10 occasions), the latter associated with a higher pain score than other attacks. Living with or being related to a migraineur decreased nonmigraineurs' ability to participate in home/family life (moderate/great impact 49%) and social/leisure activities (moderate/great impact 47%). In a tradeoff analysis, 60% of treatment choice was driven by formulation type and 40% was driven by speed of onset. As migraine disability increased, speed of onset became more important. CONCLUSIONS: This study confirms the significant burden of migraine on patients and families/cohabitants, highlighting not only reduced productivity and absences from work/school, but also time missed from family/social occasions. Many women identify menstruation to be associated with more painful attacks. Overall, in terms of treatment choice, formulation type was a more important driver than speed of onset; however, as migrainerelated disability escalates, speed of onset becomes more important. To optimise migraine management, treatment choice should be based on individual patients' needs and preferences.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".