Greater small nerve fibre damage in the skin and cornea of type 1 diabetic patients with painful compared to painless diabetic neuropathy
Bibliographic record
Abstract
BACKGROUND AND AIM: Damage to small nociceptive fibres may contribute to painful diabetic neuropathy. We aimed to compare large and small nerve fibre measurements together with skin biopsy and corneal confocal microscopy in patients with type 1 diabetes and painful or painless diabetic neuropathy. METHODS: We have assessed the McGill pain questionnaire, neuropathy disability score, vibration perception threshold, warm and cold sensation thresholds, electrophysiology, corneal confocal microscopy and skin biopsy in participants with type 1 diabetes and painful (n = 41) or painless (n = 50) diabetic neuropathy and control subjects (n = 50). RESULTS: The duration of diabetes, body mass index, glycated haemoglobin (HbA1c), blood pressure and lipid profile did not differ between subjects with painful and painless neuropathy. Neuropathy disability score and vibration perception threshold were higher and sural nerve conduction velocity was lower, but sural nerve amplitude, peroneal nerve amplitude and conduction velocity and cold and warm sensation thresholds did not differ between patients with painful compared to painless diabetic neuropathy. However, intraepidermal nerve fibre density, corneal nerve fibre density, corneal nerve branch density and corneal nerve fibre length were significantly lower in subjects with painful compared to painless diabetic neuropathy. CONCLUSIONS: There is evidence of more severe neuropathy, particularly small fibre damage in the skin and cornea, of patients with painful compared to painless diabetic neuropathy.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".