O5‐03‐05: RETINAL NERVE FIBER LAYER THINNING IN PRE‐CLINICAL ALZHEIMER'S DISEASE PREDICTS CSF AMYLOID/TAU CLASSIFICATION
Bibliographic record
Abstract
Preclinical Alzheimer's disease (AD) describes individuals with AD pathology who are cognitively healthy. We have shown that preclinical AD can be detected more sensitively and specifically by the beta amyloid 42 (Aß42)/Tau ratio relative to the concentration of these cerebrospinal fluid (CSF) markers independently (Harrington et al 2013). The purpose of this study was to investigate whether retinal thickness changes in preclinical AD are predictive of Aß42/Tau CSF classification. For this prospective study, 48 eyes from 27 pre-clinical AD participants (mean age: 75.2 ± 8.4 years) were compared to 32 eyes from 16 age-matched controls (mean age: 75 ± 8 years; p=0.45). Optical coherence tomography scans were acquired for the retinal nerve fiber layer (RNFL), ganglion cell-inner plexiform layer (GC-IPL), and the full macular thickness, comprising all retinal layers. Thicknesses of these 3 regions were compared between the two cohorts using mixed model repeated measures with unstructured covariance. Multivariable logistic regression was used to identify the RNFL region and side that best predicted amyloid/tau classification. The least-squares mean thickness (95% CI) in the RNFL, adjusted for side and region, was 9.8 (4.4, 15.3) μm thinner in the abnormal relative to normal CSF Aß42/Tau group (p<0.001). Reduced thickness was not significant in the GC-IPL or full macula. Based only on RNFL thickness, multivariable logistic regression identified the nasal and temporal retinal regions of the right eye as independent significant predictors of group association. A predicted event probability cutoff of 0.46 yielded 87% sensitivity and 56% specificity in classifying cognitively healthy individuals with abnormal CSF Aß42/Tau ratios.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".