Corneal nerve and nerve conduction abnormalities in children with type 1 diabetes
Bibliographic record
Abstract
OBJECTIVE: In vivo corneal confocal microscopy (CCM) is a novel, rapid, and non-invasive technique that identifies early small fiber damage and can predict the progression and development of clinical neuropathy in adults with type 1 diabetes. However, its usefulness in children is not well established. This study compared corneal confocal microscopy with neuropathic symptoms, signs, and objective measures of neuropathy for the diagnosis of diabetic neuropathy in children with type 1 diabetes. RESEARCH DESIGN AND METHODS: A total of 83 children with type 1 diabetes and 83 healthy participants of similar age underwent assessment of neuropathy symptoms, signs, nerve conduction studies, quantitative sensory and autonomic function testing, and in vivo CCM. RESULTS: Only of 3/83 (4%) children with type 1 diabetes had subclinical neuropathy. However, corneal nerve fiber density (p = 0.001), branch density (p = 0.006), fiber length (p = 0.002), tibial motor nerve amplitude and conduction velocity, and sural sensory nerve amplitude and conduction velocity (all p < 0.004) were lower in participants with type 1 diabetes than in the controls. Vibration, cooling, and warm perception thresholds and deep breathing heart rate variability were not found to be different (all p > 0.05) between children with type 1 diabetes and healthy controls. Multivariate regression analysis identified a possible association between body mass index and decreased corneal nerves. CONCLUSIONS: Decreased corneal nerves and abnormal nerve conduction were found in children with type 1 diabetes. CCM may allow rapid objective detection of subclinical diabetic neuropathy in children and adolescents with type 1 diabetes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".