Corneal Confocal Microscopy Detects Corneal Nerve Damage in Patients Admitted With Acute Ischemic Stroke
Bibliographic record
Abstract
Background and Purpose— Corneal confocal microscopy can identify corneal nerve damage in patients with peripheral and central neurodegeneration. However, the use of corneal confocal microscopy in patients presenting with acute ischemic stroke is unknown. Methods— One hundred thirty patients (57 without diabetes mellitus [normal glucose tolerance], 32 with impaired glucose tolerance, and 41 with type 2 diabetes mellitus) admitted with acute ischemic stroke, and 28 age-matched healthy control participants underwent corneal confocal microscopy to quantify corneal nerve fiber density, corneal nerve branch density, and corneal nerve fiber length. Results— There was a significant reduction in corneal nerve fiber density, corneal nerve branch density, and corneal nerve fiber length in stroke patients with normal glucose tolerance ( P <0.001, P <0.001, P <0.001), impaired glucose tolerance ( P =0.004, P <0.001, P =0.002), and type 2 diabetes mellitus ( P <0.001, P <0.001, P <0.001) compared with controls. HbA1c and triglycerides correlated with corneal nerve fiber density ( r =−0.187, P =0.03; r =−0.229 P =0.01), corneal nerve fiber length ( r =−0.228, P =0.009; r =−0.285; P =0.001), and corneal nerve branch density ( r =−0.187, P =0.033; r =−0.229, P =0.01). Multiple linear regression showed no independent associations between corneal nerve fiber density, corneal nerve branch density, and corneal nerve fiber length and relevant risk factors for stroke. Conclusions— Corneal confocal microscopy is a rapid noninvasive ophthalmic imaging technique that identifies corneal nerve fiber loss in patients with acute ischemic stroke.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".