Cornea Nerves Can Identify Different Types of Parkinson's Disease
Bibliographic record
Abstract
Purpose: To investigate whether the cornea nerve can distinguish between different subtypes of Parkinson's disease. Methods: A total of 63 patients diagnosed with Parkinson's disease-comprising tremor-dominant (TD), postural instability and gait disturbance (PIGD), and mixed subtypes-were included alongside 31 age- and gender-matched control participants. All participants underwent In vivo confocal microscopy (IVCM) examinations along with comprehensive assessments of clinical neurological symptoms using the Movement Disorders Society Unified Parkinson's Disease Rating Scale, Hoehn and Yahr stages, and Montreal Cognitive Assessment scores. The detection range of IVCM includes the indicators of central and inferior whorl-like cornea nerve. Results: This study involved 63 patients, 23 were classified as having the TD type, 30 as having the PIGD type, and 10 as mixed type. Among them, most of central and whorl-like corneal nerve indicators were significantly lower in the PIGD group compared to the TD group. Receiver operating characteristic analysis demonstrated that combined central and inferior whorl-like corneal nerve indicators exhibited high discriminatory power between TD and PIGD types, with an area under the curve of 0.969. Conclusions: As a non-invasive examination method, IVCM holds significant value for differentiating Parkinson's disease subtypes and identifying patients with varying motor manifestations. Among these findings, individuals with PIGD displayed more pronounced corneal nerve damage; furthermore, patients exhibiting lower inferior whorl length, corneal nerve fiber width, and fractal dimension of corneal nerves values were found to be at greater risk of being classified within the PIGD subtype.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".