Cognitive performance in young and middle-aged adults with migraine: Investigating the correlation with white matter hyperintensities and psychological symptoms
Bibliographic record
Abstract
INTRODUCTION: This study aimed to evaluate the cognitive performance of migraine patients with (MwA) and without aura (MwoA) and investigate the correlation of white matter hyperintensities (WMHs) and psychological symptoms with their cognitive test scores. MATERIAL AND METHODS: Hundred migraine patients aged 20-55 years and 80 healthy volunteers with similar age, sex, and education level were enrolled. The total Montreal Cognitive Assessment (MoCA) scores were compared by age, sex, presence of aura, migraine duration, attack frequency, pain localization, presence and number of WMHs, and the scores of the Beck Depression Inventory and the Beck Anxiety Inventory (BAI). RESULTS: Forty-seven (47%) patients had MwA and 53 (53%) had MwoA. The performance of the MwA patients was significantly poorer than that of the MwoA patients and the healthy subjects on the MoCA scales. In particular, the results revealed lower scores in the subscales regarding visuospatial/executive functions, naming, memory, attention, and abstraction in MwA patients than in the MwoA patients. Compared to healthy controls, more number of migraine patients had WMHs. The presence and number of WMHs had no significant correlation with the MoCA scores of the migraine patients. There was a significant correlation of the BAI and BDI scores with the total MoCA scores considering all migraine patients. CONCLUSIONS: This study suggested that MwA may be associated with low cognitive performance which was correlated with depression and anxiety but not with WMHs. Further, longitudinal studies for assessing the relationship between WMHs, cognitive functions, and migraine, and for establishing the causality are warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".