Subjective Cognitive Decline Patterns in Patients with Migraine, with or without Depression, versus Non-depressed Older Adults
Bibliographic record
Abstract
Purpose: Cognitive decline is a common complaint in young patients with migraine, especially those with depression. Independent of psychiatric factors such as depression, subjective cognitive decline (SCD) is associated with an elevated risk of progression to dementia. This study aimed to investigate patterns of subjective cognitive complaints between migraineurs with or without depression and non-depressed older adults.Methods: This retrospective study included 331 outpatients with SCD (293 from a headache clinic and 38 from a memory clinic). SCD was diagnosed as “yes” based on two questions about SCD. The Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to assess cognitive function. The SCD Questionnaire (SCD-Q) with three subdomains was analyzed to compare SCD between groups.Results: Among patients with SCD, significant differences in duration of education were found among the groups—specifically, migraineurs with depression (12.39 years) had longer education than non-depressed older adults (10.50 years) and shorter education than migraineurs without depression (14.28 years). The total MMSE and MoCA scores did not differ between migraineurs with and without depression. Regarding SCD-Q scores, migraineurs with depression showed higher scores overall and in all cognitive domains than migraineurs without depression, with no significant difference compared to non-depressed older adults. Conclusion: Although the depressed migraineurs with SCD were younger and more educated than the non-depressed older adults with SCD, both groups reported similarly high levels of SCD. Higher levels of surveillance for cognitive decline are warranted for migraineurs with depression who have SCD.
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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".