In Vivo Corneal Confocal Microscopy Detects Improvement of Corneal Nerve Parameters following Glycemic Control in Patients with Type 2 Diabetes
Bibliographic record
Abstract
Aim. This study aimed to investigate whether in vivo corneal confocal microscopy (CCM) can detect the improvement of corneal nerve parameters following glycemic control in patients with type 2 diabetes in natural history. Methods. Thirty-two patients with diabetes complicated by DPN and 12 age-matched control subjects underwent detailed clinical examination and were assessed per the Toronto Clinical Scoring Scale for DPN, nerve conduction studies, and IVCCM at baseline and after approximately one year from the first visit. Results. At follow-up, 16 diabetic patients had improved glycemic control (group A, HbA1c < 7.0%, 7.78 ± 1.62% versus 6.52 ± 0.59%, P=0.005 ), while the remainder continued to have elevated HbA1c levels (group B, HbA1c ≥ 7.0%, 8.55 ± 1.57% versus 8.79 ± 1.05%, P=0.527 ). For patients in group A, corneal nerve fiber density (CNFD) (18.55 ± 5.25 n/mm2 versus 21.78 ± 6.13 n/mm2, P=0.005 ) and corneal nerve fiber length (CNFL) (11.62 ± 2.89 mm/mm2 versus 13.04 ± 2.44 mm/mm2, P=0.029 ) increased significantly compared to baseline. For patients in group B, sural sensory nerve conduction velocity (47.93 ± 7.20 m/s versus 44.67 ± 6.43 m/s, P=0.024 ), CNFD (17.19 ± 5.31 n/mm2 versus 15.67 ± 4.16 n/mm2, P=0.001 ), corneal nerve branch density (19.33 ± 12.82 n/mm2 versus 14.23 ± 6.56 n/mm2, P=0.033 ), and CNFL (11.16 ± 2.57 mm/mm2 versus 9.90 ± 1.75 mm/mm2, P=0.011 ) decreased significantly. Conclusions. The results of this study suggest that morphological repair of corneal nerve fibers can be detected when glycemic control improves. In vivo CCM could be a sensitive method that can be applied in future longitudinal or interventional studies on DPN.
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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 | 0.000 | 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 | 0.000 |
| 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".