DETERMINING MIGRAINE WITH AURA TRIGGER FACTORS TOWARD IMPROVING THE PRACTICE: A CROSS-SECTIONAL STUDY FROM SAUDI ARABIA
Bibliographic record
Abstract
Background: Primary headache is one of the most common disorders treated in neurology clinics. Globally, it has been estimated that the current prevalence of headache disorder in adults symptomatic, at least once within the last year, is about 50%. According to the Global Burden of Disease Study 2015, migraine was ranked as the third most common cause of disability worldwide. One of the significant migraines is migraine with aura, a frequent headache that occurs after or with sensory disturbances. Aim: This study aimed to identify and compare triggers in previously diagnosed patients with migraine with aura (MwA) to those who have not been identified with migraine with aura before but diagnosed according to a questionnaire survey distributed in the Kingdom of Saudi Arabia (KSA). Methods: Adults aged 18–50 years were randomly selected in the Kingdom of Saudi Arabia to participate in this non-interventional, multicentre, cross-sectional study. The study data was collected via an online questionnaire using the diagnostic criteria of the International Classification of Headache Disorders 2018 and the Cleveland Clinic Canada headache questionnaire. Results and Discussion: Of the 4.140 potentially eligible individuals, 302 were not previously diagnosed with migraine with aura but were diagnosed with a questionnaire (Group A), while 71 participants were already diagnosed (MwA) (Group B). A significant relationship was found among MwA and stress and anxiety, sleep disturbance, noisy places, exposure to lots of lights, prolonged computer work, hunger, fasting, studying for exams, and menstruation in Group A. Group B experienced all of the previous triggers in addition to weather changes, certain odors, and the consumption of cheese, bananas, and hotdogs. Conclusions: The trigger factors are common in individuals with MwA. Awareness of these triggers is essential for preventing the severe complications of MwA, hence, enhance the quality of life of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".