Impact of monthly headache days on migraine‐related quality of life: Results from the Chronic Migraine Epidemiology and Outcomes (CaMEO) study
Bibliographic record
Abstract
OBJECTIVE: To characterize the direct impact of monthly headache days (MHDs) on health-related quality of life (HRQoL) in people with migraine and the potential mediating effects of anxiety, depression, and allodynia. BACKGROUND: Although the general relationship between increased migraine frequency (i.e., MHDs) and reduced HRQoL is well established, the degree to which reduced HRQoL is due to a direct effect of increased MHDs or attributable to mediating factors remains uncertain. METHODS: Cross-sectional baseline data from participants with migraine who completed the Core and Comorbidities/Endophenotypes modules in the 2012-2013 US Chronic Migraine Epidemiology and Outcomes (CaMEO) study, a longitudinal web-based survey study, were analyzed. The potential contribution of depression, anxiety, and/or allodynia to the observed effects of MHDs on HRQoL as measured by the Migraine-Specific Quality-of-Life Questionnaire version 2.1 (MSQ) was evaluated. RESULTS: A total of 12,715 respondents were included in the analyses. The MSQ domain scores demonstrated progressive declines with increasing MHD categories (B = -1.23 to -0.60; p < 0.001). The observed HRQoL decrements associated with increasing MHDs were partially mediated by the presence of depression, anxiety, and allodynia. The MHD values predicted 24.0%-32.4% of the observed variation in the MSQ domains. Depression mediated 15.2%-24.3%, allodynia mediated 9.6%-16.1%, and anxiety mediated 2.3%-6.0% of the observed MHD effects on the MSQ. CONCLUSIONS: Increased MHD values were associated with lower MSQ scores; the impact of MHDs on the MSQ domain scores was partially mediated by the presence of depression, anxiety, and allodynia. MHDs remain the predominant driver of the MSQ variation; moreover, most of the variation in the MSQ remains unexplained by the variables we analyzed. Future longitudinal analyses and studies may help clarify the contribution of MHDs, comorbidities, and other factors to changes in HRQoL.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".