Correlations between retinal nerve fiber layer thickness and cognitive progression in Parkinson's disease: A longitudinal study
Bibliographic record
Abstract
BACKGROUND: Retinal abnormalities measured by optical coherence tomography (OCT) have been detected in both Parkinson's disease (PD) and Alzheimer's disease (AD). Cognitive impairment is not only found in AD, but 75-90% of PD patients will also develop dementia in the late stage of disease. We assessed whether baseline retinal nerve fiber layer (RNFL) thickness predicted worsening of cognitive status over time and the correlation between RNFL thickness and the detailed impaired cognitive domains in PD. METHODS: RNFL thickness was measured using high-definition OCT in 78 non-dementia PD patients. Clinical and cognitive assessments were performed at baseline and at 3.61 ± 0.65 years follow-up. Linear mixed-effects models were used to examine associations between RNFL thickness and the changes in cognitive test scores, after adjusting for age, sex, disease duration and education. RESULTS: Analysis of outcomes according to baseline RNFL tertiles showed worse performance in global cognitive tests, delayed memory, and executive functions in patients with a thin RNFL. During follow-up, greater cognitive deterioration was found in thin RNFL tertile patients. Lower baseline average RNFL thickness was associated with greater annualized decline in Mini-Mental State Examination and Montreal Cognitive Assessment. CONCLUSION: The correlation between RNFL thickness and cognitive dysfunction suggests that OCT may be useful for predicting cognitive dysfunction in PD patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".