Evaluation of retinal nerve fiber layer and ganglion cell layer thickness in Alzheimer's disease using optical coherence tomography
Bibliographic record
Abstract
Abstract Background: To evaluate the feasibility of using optical coherence tomography (OCT) for the detection of Alzheimer's disease (AD), by measuring the thickness of the retinal nerve fiber layer (RNFL) and the ganglion cell layer and inner plexiform layer (GCL-IPL). Methods: This was a single-center, cross-sectional study. The study included 29 patients with AD (mean age ± standard deviation: 75.61 ± 6.24 years) and 29 healthy age- and sex-matched controls. All participants underwent cognitive evaluations using the Montreal Cognitive Assessment test. Measurements of the RNFL thickness, as well as GCL-IPL thickness, were obtained for all participants using OCT. Both RNFL and GCL-IPL parameters were adjusted for best–corrected visual acuity. Results: The mean RNFL thickness was significantly thinner in the AD group than in the control group (85.24 and 90.68 µm, respectively, adjusted P = 0.019). The superior quadrant was thinner in the AD group (adjusted P = 0.042). The thicknesses did not differ significantly between groups for the other quadrants. The mean GCL-IPL thickness in the AD (68.81 µm) was significantly thinner than that in the controls (76.42 µm) (adjusted P = 0.010). Overall, there was a negative correlation between age and mean RNFL; and between age and GCL-IPL thickness (r = − 0.338, P = 0.010 and r = − 0.346, P = 0.008, respectively). Conclusions: The mean RNFL and GCL-IPL thicknesses were thinner in the AD group than in the control group. These findings suggest that RNFL and GCL-IPL thickness may be biological markers for AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.006 |
| 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 teacher head, 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".