Rapid Corneal Nerve Fiber Loss: A Marker of Diabetic Neuropathy Onset and Progression
Bibliographic record
Abstract
OBJECTIVE Corneal nerve fiber length (CNFL) represents a biomarker for diabetic distal symmetric polyneuropathy (DSP). We aimed to determine the reference distribution of annual CNFL change, the prevalence of abnormal change in diabetes, and its associated clinical variables. RESEARCH DESIGN AND METHODS We examined 590 participants with diabetes (399 with type 1 diabetes [T1D] and 191 with type 2 diabetes [T2D]) and 204 control patients without diabetes with at least 1 year of follow-up and classified them according to rapid corneal nerve fiber loss (RCNFL) if CNFL change was below the 5th percentile of the control patients without diabetes. RESULTS Control patients without diabetes were 37.9 ± 19.8 years old, had median follow-up of three visits over 3.0 years, and mean annual change in CNFL was −0.1% (90% CI −5.9% to 5.0%). RCNFL was defined by values exceeding the 5th percentile of 6% loss. Participants with T1D were 39.9 ± 18.7 years old, had median follow-up of three visits over 4.4 years, and mean annual change in CNFL was −0.8% (90% CI −14.0% to 9.9%). Participants with T2D were 60.4 ± 8.2 years old, had median follow-up of three visits over 5.3 years, and mean annual change in CNFL was −0.2% (90% CI −14.1% to 14.3%). RCNFL prevalence was 17% overall and was similar by diabetes type (64 T1D [16.0%], 37 T2D [19.4%], P = 0.31). RNCFL was more common in those with baseline DSP (47% vs. 30% in those without baseline DSP, P = 0.001), which was associated with lower peroneal conduction velocity but not with baseline HbA1c or its change over follow-up. CONCLUSIONS An abnormally rapid loss of CNFL of 6% per year or more occurs in 17% of diabetes patients. RCNFL may identify patients at highest risk for the development and progression of DSP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".