Structure-Function Relationship Between Corneal Nerves and Conventional Small-Fiber Tests in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE: In vivo corneal confocal microscopy (IVCCM) has been proposed as a noninvasive technique to assess small nerve fiber structural morphology. We investigated the structure-function relationship of small fibers in diabetic sensorimotor polyneuropathy (DSP). RESEARCH DESIGN AND METHODS: Ninety-six type 1 diabetic subjects with a spectrum of clinical DSP and 64 healthy volunteers underwent IVCCM examinations to determine corneal nerve structure, including corneal nerve fiber length (CNFL), fiber density (CNFD), branch density (CNBD), and fiber tortuosity (CNFT). Small nerve fiber function was assessed by cooling detection thresholds (CDTs), axon reflex-mediated neurogenic vasodilatation in response to cutaneous heating by laser Doppler imaging flare technique (LDIFLARE), and heart rate variability (HRV). Linear associations between structural and functional measures in type 1 diabetic subjects were determined using Spearman correlation coefficients and linear regression analysis. RESULTS: Of the type 1 diabetic subjects, with a mean age of 38.2 ± 15.5 years and a mean HbA1c of 7.9 ± 1.4%, 33 (34%) had DSP according to the consensus definition. Modest correlations were observed between CNFL, CNFD, and CNBD and all functional small-fiber tests (rs = 0.25 to 0.41; P ≤ 0.01 for all comparisons). For example, quantitatively every 1 mm/mm(2) lower CNFL was associated with a 0.61°C lower CDT, a 0.07 cm(2) lower LDIFLARE area, and a 1.78% lower HRV. No significant associations were observed for CNFT and the functional small-fiber measures. CONCLUSIONS: Small nerve fiber structural morphology assessed by IVCCM correlated well with functional measures of small nerve fiber injury. In particular, CNFL, CNFD, and CNBD demonstrated clear structure-function relationships.
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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 | 0.002 |
| 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 | 0.000 | 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".