Correlating OCT Changes With Disease Severity And Cognitive Status In Parkinson’s Disease (P1.012)
Bibliographic record
Abstract
Objective: To correlate changes in average macula volume and average retinal nerve fiber layer (RNFL) thickness as measured by optical coherence tomography (OCT) with clinical outcomes and MRI measures in Parkinson’s disease (PD) patients with and without mild cognitive impairment (MCI), and in age- and sex-matched healthy controls (HCs). Background: Because the dopaminergic cells in the retina lack a myelin sheath, it is believed that studying the RNFL can provide insight into neurodegeneration and thus be potential biomarkers for disease progression in PD. Methods: Twenty-five HCs and 40 PD patients underwent clinical, cognitive, OCT and 3T MRI examinations. PD patients were divided into two groups: 23 patients of Hoehn & Yahr stage 1-2 with normal cognition [defined as Montreal Cognitive Assessment (MoCA) score > 26 and Clinical Dementia Rating (CDR) of 0] were placed in the non-MCI group and 17 patients of Hoehn & Yahr stage 2-4 with altered cognition (MoCA scores <26 and CDR of 0.5) were placed in the MCI group. MRI measures assessed lesion, atrophy diffusion and iron deposition outcomes. Correlations were performed between OCT measures and clinical, cognitive and MRI outcomes between PD and HC and MCI and non-MCI groups. Results: No significant differences in RNFL thickness or macula volume between PD patients and HC or between MCI and non-MCI PD patients was found. There were no significant correlations between OCT and clinical, cognitive and MRI outcomes between PD and HC and between MCI and non-MCI groups. Conclusions: In this study, OCT status did not differentiate between PD and HC or between MCI and non-MCI PD groups. OCT was also not related to clinical, cognitive or MRI outcomes. A larger study with more subjects or longitudinal studies are required to further investigate whether RNFL is affected in PD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".