Correlation between retinal nerve fibre layer thickness and white matter lesions in Alzheimer's disease
Bibliographic record
Abstract
OBJECTIVES: Early diagnosis in Alzheimer's disease (AD) is crucial in order to implement new therapeutic strategies. The retina is embryologically related to the brain. Thus, the possible usefulness of optical coherence tomography (OCT) in the early detection of AD is currently being studied. Our aim was to study the relationship between retinal nerve fiber layer (RNFL) thickness and AD. METHODS: We undertook an observational, analytical, cross-sectional study with consecutive sampling of 32 patients with AD or mild cognitive impairment and a group of healthy controls (C). The total number of eyes studied was 64. An ophthalmological and a comprehensive neuropsychological evaluation were performed in all participants. Quantification of white matter lesions and study of atrophy of the hippocampus by cerebral magnetic resonance were also performed. RESULTS: We observed a significant linear trend towards a thinning of RNFL as the degree of cognitive deterioration increased, in the superior and temporal quadrants of the retina. A significant correlation was also noted between the mean thickness of the RNFL of the left temporal quadrant and occipital white matter lesions (r = -0.579, p = 0.038). CONCLUSIONS: OCT could be a safe, rapid noninvasive tool providing useful biomarkers in the early detection of cognitive deterioration and AD.
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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) | 0.000 | 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".