Corneal confocal microscopy detects small fibre neurodegeneration in Parkinson's disease using automated analysis
Bibliographic record
Abstract
Abstract We studied the utility of corneal confocal microscopy (CCM) in detecting a reduction in corneal nerve parameters in a large cohort of patients with Parkinson’s disease (PD) compared to controls using a fully automated potentially scalable method of analysis. We also assessed if CCM parameters are related to the severity and sub-type of PD. 98 participants with PD and 26 healthy controls underwent CCM with automated corneal nerve quantification , MDS-UPDRS III, Hoehn and Yahr scale, Montreal Cognitive Assessment, Parkinson’s Disease Questionnaire-39 and PD subtype assessment. Corneal nerve fibre density (mean difference: − 5.00 no/mm 2 , 95% confidence interval (CI) [− 7.89, − 2.12], p = 0.001), corneal nerve branch density (mean difference: − 10.71 no/mm 2 , 95% CI [− 16.93, − 4.48], p = 0.003), corneal total branch density (mean difference: − 14.75 no/mm 2 , 95% CI [− 23.58, − 5.92], p = 0.002), and corneal nerve fibre length (mean difference: − 2.57 mm/mm 2 , 95% CI [− 4.02, − 1.12], p = 0.001) were significantly lower in PD participants compared to controls. There was no correlation between corneal nerve parameters and duration, severity or subtype of PD, cognitive function or quality of life. CCM with automated corneal nerve analysis identifies nerve fibre damage and may act as a biomarker for neurodegeneration in PD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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".