Using in vivo corneal confocal microscopy to identify diabetic sensorimotor polyneuropathy risk profiles in patients with type 1 diabetes
Bibliographic record
Abstract
Objective Diabetic sensorimotor peripheral neuropathy (DSP) is the most prevalent complication in diabetes mellitus. Identifying DSP risk is essential for intervening early in the natural history of the disease. Small nerve fibers are affected earliest in the disease progression and evidence of this damage can be identified using in vivo corneal confocal microscopy (IVCCM). Research design and methods We applied IVCCM to a cohort of 40 patients with type 1 diabetes to identify their DSP risk profile. We measured standard IVCCM parameters including corneal nerve fiber length (CNFL), and performed nerve conduction studies and quantitative sensory testing. Results 40 patients (53% female), with a mean age of 48±14, BMI 28.1±5.8, and diabetes duration of 27±18 years were enrolled between March 2014 and June 2015. Mean IVCCM CNFL was 12.0±5.2 mm/mm2 (normal ≥15 mm/mm2). Ten patients (26%) without DSP were identified as being at risk of future DSP with mean CNFL 11.0±2.1 mm/mm2. Six patients (15%) were at low risk of future DSP with mean CNFL 19.0±4.6 mm/mm2, while 23 (59%) had established DSP with mean CNFL 10.5±4.5 mm/mm2. Conclusions IVCCM can be used successfully to identify the risk profile for DSP in patients with type 1 diabetes. This methodology may prove useful to classify patients for DSP intervention clinical trials.
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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 | 0.001 |
| 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".