Assessing Corneal Confocal Microscopy and Other Small Fiber Measures in Diabetic Polyneuropathy
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Damage to small nerve fibers is common in diabetic polyneuropathy (DPN), and the diagnosis of DPN relies on subjective symptoms and signs in a combination with objective confirmatory tests, typically electrophysiology or intraepidermal nerve fiber density (IENFD) from skin biopsy. Corneal confocal microscopy (CCM) has been introduced as a tool to detect DPN. However, it is unclear if CCM can reliably be used to diagnose DPN and how the technique compares with other commonly used measures of small fiber damage, such as IENFD, cold detection threshold (CDT), and warm detection threshold (WDT). Therefore, we assessed and compared the use of CCM, IENFD, CDT, and WDT in the diagnosis of DPN in patients with type 2 diabetes. METHODS: In this cohort study, the participants underwent detailed neurologic examination, electrophysiology, quantification of IENFD, CCM, and quantitative sensory testing. Definition of DPN was made in accordance with the Toronto criteria for diabetic neuropathy (without relying on IENFD and thermal thresholds). RESULTS: = 0.003, and <0.005, respectively), whereas there was no difference between controls and patients with diabetes without DPN. All 3 CCM measures showed a very low diagnostic sensitivity with CNFL showing the highest (14.4% [95% CI 9.8-18.4]) and a specificity of 95.7% (88.0-99.1). In comparison, the sensitivity of abnormal CDT and/or WDT was 30.5% (24.4-37.0) with a specificity of 84.9% (74.6-92.2). The sensitivity of abnormal IENFD was highest among all measures with a value of 51.1% (43.7-58.5) and a specificity of 90% (79.5-96.2). CCM measures did not correlate with IENFD, CDT/WDT, or neuropathy severity in the group of patients with DPN. DISCUSSION: CCM measures showed the lowest sensitivity compared with other small fiber measures in the diagnosis of DPN. This indicates that CCM is not a sensitive method to detect DPN in recently diagnosed type 2 diabetes. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that CCM measures aid in the detection of DPN in recently diagnosed type 2 diabetics but with a low sensitivity when compared with other small fiber measures.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".