Retinal Nerve Fiber Layer Thickness is Associated with Episodic Memory Deficit in Mild Cognitive Impairment Patients
Bibliographic record
Abstract
Changes in retinal nerve fiber layer (RNFL) thickness have been reported in patients with mild cognitive impairment (MCI), the pre-dementia stage of Alzheimer's disease (AD). However, whether RNFL thickness is associated with specific cognitive impairment of MCI patients remains unknown. Therefore, we set out to investigate the potential association between RNFL thickness and episodic memory in MCI patients. Seventy five older adults (mean age 74 ± 3 years, 55% men) were included in the study. Fifty-two participants had normal cognition (NC), and 23 participants were diagnosed with MCI. RNFL thickness was obtained by optical coherence tomography measurement. Cognitive function was evaluated by the Repeatable Battery for the Assessment of Neuropsychological Status on the same day of the optical examination. We found that nasal quadrant RNFL thickness was positively associated with episodic memory scores in the participants with normal cognition: word list learning (r=0.392, p=0.004) and story recall (r=0.307, p=0.027). In the participants with MCI, however, the inferior quadrant RNFL thickness was inversely associated with the episodic memory score: word list learning (r=-0.652, p=0.001), story memory (r=-0.429, p=0.041), and story recall (r=-0.502, p=0.015,). The findings from this pilot study suggest that the inferior quadrant RNFL thickness was associated with specific episodic memory in MCI patients and could serve as a biomarker of MCI and AD. These findings would promote more studies to determine the potential application of RNFL as an AD biomarker.
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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 | 0.001 | 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".