Candidate Retinal Biomarkers in <scp>CNS</scp> Neurodegenerative Disease
Bibliographic record
Abstract
Summary To institute early prevention of the common CNS neurodegenerative diseases, including Alzheimer's Disease ( AD ), Parkinson's Disease ( PD ), Lewy Body Disease and Vascular Cognitive Disorders, cost‐effective, non‐invasive early diagnostic biomarkers are essential. Early detection of these diseases is critical given the growing evidence that new therapies will only be effective in pre‐symptomatic or prodromal stages of the degenerative process. Retinal imaging by spectral domain optical coherence tomography is currently used to evaluate morphological neurodegenerative changes caused by ophthalmic disease. Evidence suggests that this technique may also provide a biomarker in AD and PD , revealing changes in the retinal nerve fibre layer that correlate with cortical thinning and possibly prior to emergence of clinical symptoms. This presentation will up‐date the evidence supporting the use of non‐invasive retinal imaging as a pre‐symptomatic prognostic biomarker of CNS neurodegenerative disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".