Correlation between microvascular changes in the retina and choroid with disease duration, severity, and cognitive deficits in migraine with and without aura
Bibliographic record
Abstract
Background: The thickness of the retina and choroid, as measured by optical coherence tomography (OCT), may reflect neurovascular changes in migraine patients. Objective: To evaluate the relationships between the changes identified by OCT and optical coherence tomography angiography (OCTA) in patients with migraine with aura (MA) or migraine without aura (MWA) and the duration and severity of the disease using the Headache Impact Test-6 (HIT-6) questionnaire and cognitive assessment through the Montreal Cognitive Assessment (MoCA). Methods: In this cross-sectional study, 16 patients with MA and 15 patients with MWA were compared with 16 healthy age-matched controls. Results: Foveal choroidal thickness was lower in individuals with migraine than in individuals in the control group (p<0.05). Statistically significant negative correlations were found between disease duration and total macular thickness (p=0.037; compared with the average thickness of the MWA group), ganglion cell complex (GCC) thickness (p=0.017; compared with the average thickness of the MWA group) and choroidal thickness (p=0.039; compared with the average thickness of the MWA group), as well as the HIT-6 score and the peripapillary retinal nerve fiber layer (pRNFL) thickness (p=0.027; compared with the mean thickness of the MWA group). Conclusion: Compared with controls, individuals with migraine showed a significant reduction in choroidal thickness. Our results showed that the longer the disease duration was, the thinner the total macula, GCC and choroidal thickness were. Additionally, the thickness of the pRNFL layer showed an inverse correlation with the disability caused by migraine.
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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 | 0.000 |
| 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) | 0.002 | 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".