Correlation between retinal nerve fiber layer thickness and cognitive function in patients with mild ischemic stroke
Bibliographic record
Abstract
Objective To investigate the association between retinal nerve fiber layer (RNFL) thickness and cognitive dysfunction in patients with mild ischemic stroke. Methods Total 146 patients with mild ischemic stroke that diagnosed and treated in Chengde Central Hospital in Hebei from January 2020 to December 2021 were included. Optical coherence tomography (OCT) was used to measure the RNFL thickness in each quadrant of both eyes, and Mini⁃Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) were used to evaluate cognitive function. Results In patients with mild ischemic stroke, the RNFL thickness at the superior side of left eye was greater than right eye (t = ⁃ 4.589, P = 0.000), and the RNFL thickness at the temporal side of right eye was greater than left eye (t = 3.639, P = 0.000). Correlation analysis showed that the correlation between the RNFL thickness at the superior side of the left eye and the MMSE score (r = 0.385, P = 0.000), the RNFL thickness at the temporal side of the left eye and the National Institutes of Health Stroke Scale (NIHSS) score at admission (r = 0.170, P = 0.020) were positively correlated. The correlation between the RNFL thickness at the inferior side of the left eye and the history of drinking (r = ⁃ 0.216, P = 0.011), the RNFL thickness at the temporal side of the right eye and the history of hypertension (r = ⁃ 0.194, P = 0.023) were negativley correlated. Conclusions The thinning of RNFL in patients with mild ischemic stroke may have a certain degree of association with cognitive dysfunction.
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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) | 0.001 | 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".