Real-World experience of interictal burden and treatment in migraine: a qualitative interview study
Bibliographic record
Abstract
Abstract Background: The debilitating nature of migraine attacks is widely established; however, less is known about how the interictal burden (i.e., how patients are affected in-between migraine episodes) of migraine impacts on patients’ health-related quality of life (HRQL). Acute and preventive treatments may lift the burden of the disease, but they often have unwanted side effects and limited effectiveness. The objective of this study was to understand the interictal burden of migraines, from the patient perspective, and to explore patient experience with migraine treatments. Methods: Participants (n=35) with a self-reported diagnosis of migraine were recruited in the US, UK and Canada, including a subgroup of patients who had taken calcitonin gene-related peptide monoclonal antibody (CGRP mAb) treatment for ≥3 months. Participants completed a background questionnaire, followed by a semi-structured interview via telephone or video call. The interviews explored patients’ migraine symptoms, perception of interictal burden and treatment experience. The interview transcripts were analysed using thematic analysis.Results: The most reported migraine symptom was migraine pain, followed by aura, sensory sensitivity and nausea. Most participants reported interictal impact on HRQL, lifestyle changes they made to avoid triggers or in anticipation of an attack, impacts on work, career, daily activities and relationships. Emotional impacts were reported by all participants, including anger, depression, anxiety and hopelessness. Many participants who took preventive treatments reported improvements in HRQL and functioning but still experienced breakthrough attacks. Among patients who took CGRP mAbs, participants noted varying consistency of treatment effectiveness between treatment administrations.Conclusion: This study detailed the additional HRQL impact of migraine in-between migraine attacks and described the unmet need for effective treatment options to prevent and mitigate migraine attacks.Trial registration: Not applicable
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".