Ophthalmological traits in older adult and risk of Alzheimer’s disease: results from a French geriatric cohort
Bibliographic record
Abstract
Ophthalmological changes have been reported in Alzheimer's patients. Our objectives were to determine whether: i) GCC (ganglion cell complex) and RNFL (retinal nerve fibre layer) thickness were associated with different stages of AD (i.e., no AD, prodromal AD, dementia-stage AD), and ii) GCC and RNFL thickness predicted disease progression in older non-demented patients with subjective memory complaints followed for four years. Ninety-one French older community-dwellers with memory complaint and without open-angle glaucoma or age-related macular degeneration (mean, 71.60 ± 4,73 years; 44% women) from the GAIT study underwent examination with HD-OCT, measuring the thickness of the macula, the macular GCC and the RNFL. They also had a complete cognitive diagnosis (i.e., cognitively healthy, prodromal AD, or dementia AD), and a cognitive follow-up 4 years later looking for a possible conversion. Age, sex, body mass index (BMI), number of comorbidities, and Instrumental activities of daily living (IADL) score were considered as potential confounders. At baseline, 37 (40.7%) patients were diagnosed as cognitively healthy, 47 (51.6%) as MCI, and 7 (7.7%) as AD. Mean GCC thickness was higher in cognitively healthy patients than in MCI patients (79.23 vs. 76.27 μm, p = 0.023), particularly in the inferior and nasal fields (p = 0.023 and p = 0.005, respectively). This difference was also found between cognitively healthy patients and others (MCI and AD) in the superior, inferior and nasal fields (p = 0.030, p = 0.014 and p = 0.002, respectively). There was no difference in RNFL thickness between the different cognitive statuses. After 4 years of follow-up, 12 patients (70.6%) of the 17 followed had not changed their cognitive status, while 5 (29.4%) had converted to a more advanced stage of AD. There were no significant differences between the two groups in either GCC thickness (p = 0.429) or RNFL thickness (p = 0.286). We found decreased CGG thicknesses in Alzheimer's patients at prodromal and dementia stages, compared with cognitively healthy participants. There was no association between RNFL thickness and cognitive status, nor between CCG or RNFL thicknesses and the risk of progressing to AD stages after 4 years of follow-up.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".