Cognitive flexibility among female migraine patients: case–control study
Bibliographic record
Abstract
Purpose: The aim of this study was to determine if cognition is affected in female migraine sufferers by comparing cognitive domains with a healthy control group.Materials and Methods: Fifty patients with migraine and 50 healthy controls (all female) were enrolled in this cross-sectional case–control study. The Beck Depression test, Beck Anxiety test, California Verbal Learning Test, Montreal Cognitive Assessment Scale, and Wisconsin Card Sorting Test (WCST) were performed in both groups. Results: Using a multivariate logistic regression model of migraine, WCST non-perseverative error (odds ratio [OR], 1.62; 95% confidence interval [CI], 1.028–2.568; p = 0.03), WCST percent of perseverative error (OR, 0.23; 95%CI, 0.071–0.786; p = 0.01), WCST perseverative response (OR, 4.55; 95%CI, 1.272–16.298; p = 0.02), no alcohol consumption (OR, 0.006; 95%CI, 0.000–0.943; p = 0.04), family history of hypertension (OR, 4.46; 95%CI, 1.114–17.915; p = 0.03), family history of migraine (OR, 4.028; 95%CI, 1.027–15.799; p = 0.04), and no family history of stroke (OR, 0.034; 95%CI, 0.003–0.448; p = 0.01) were significant factorsConclusion: Among WCST scores, non-perseverative error provides insight into the patient’s problem solving ability. Meanwhile, percent perseverative error and perseverative response scores provide insight into cognitive flexibility ability. Therefore, in our study group, patients with migraine show better problem solving and cognitive flexibility ability than the healthy control group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".