Quality of Life and Prevalence of Impulsivity in Females With Migraine Headaches
Bibliographic record
Abstract
Background Migraine is a common neurological disorder with significant socioeconomic and personal impact. Recent research suggests a potential association between migraine and impulsivity, particularly in females. Objective This study aims to investigate the prevalence of impulsivity and its correlation with migraine-related disability in females with migraine. Methods This is a case-control study involving female patients aged 18-60 with migraine, recruited from neurology and psychiatry outpatient clinics. Standardized assessments, including Migraine Disability Assessment (MIDAS), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder 7-item (GAD-7), Barratt Impulsiveness Scale Version 11 (BIS-11), and McLean Screening Instrument for Borderline Personality Disorder (MSI-BPD), were used to evaluate migraine disability, depression, anxiety, and impulsivity. Data analysis was performed using SPSS version 25 (IBM Corp., Armonk, NY, USA). Results There were a total of 149 participants (n=149) in the study, comprising 68 (n=68, 46%) migraine sufferers and 81 (n=81, 54.4%) controls. Migraine patients had significantly higher impulsivity (BIS-11: 34.67 ± 11.95 vs. 26.81 ± 2.99, p < 0.001) and a higher prevalence of borderline personality traits (MSI-BPD: 2.76 ± 2.65 vs. 1.23 ± 0.93, p < 0.01). Depression and anxiety were also significantly more common in the migraine group. Conclusion These findings suggest a strong association between migraine, impulsivity, and mood disorders, emphasizing the need for integrated psychological and neurological management in migraine patients.
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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 | 0.002 |
| 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) | 0.002 | 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".