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.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".