Corneal Confocal Microscopy Identifies Parkinson's Disease with More Rapid Motor Progression
Bibliographic record
Abstract
BACKGROUND: Corneal confocal microscopy (CCM) is a noninvasive, reproducible ophthalmic technique to quantify corneal small nerve fiber degeneration. CCM demonstrates small nerve fiber damage in Parkinson's disease (PD), but its role as a longitudinal biomarker of PD progression has not been explored. OBJECTIVE: The aim of this study was to assess corneal nerve morphology using CCM in relation to disease progression in PD. METHODS: Sixty-four participants with PD were assessed at baseline and at 12-month follow-up. Participants underwent CCM with automated corneal nerve quantification and assessment of Movement Disorder Society Unified Parkinson's Disease Rating Scale, Hoehn and Yahr stage, and Montreal Cognitive Assessment. RESULTS: Corneal nerve fiber density (CNFD), corneal nerve branch density, corneal nerve fiber length, corneal total branch density, and corneal nerve fiber area were significantly lower in participants with PD compared with healthy control subjects. Worsening of Movement Disorder Society Unified Parkinson's Disease Rating Scale part III score over 12 months was significantly greater in participants with a CNFD in the lowest compared with the highest quartile at baseline (mean difference: 6.0; 95% CI: 1.0-10.9; P = 0.019). There were no significant changes in CNFD, corneal nerve branch density, corneal nerve fiber length, corneal total branch density, corneal nerve fiber area, or corneal nerve fiber width between baseline and 12-month follow-up. CONCLUSIONS: CCM identifies neurodegeneration in patients with PD, especially those who show the greatest progression in neurological disability. CCM may be a useful tool to help enrich clinical trials with those likely to exhibit more rapid progression and reduce required sample size and cost of studies. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".