Does the Prevailing Hypothesis That Small-Fiber Dysfunction Precedes Large-Fiber Dysfunction Apply to Type 1 Diabetic Patients?
Bibliographic record
Abstract
OBJECTIVE: The prevailing hypothesis that early subclinical small-fiber injury precedes large-fiber damage in diabetic sensorimotor polyneuropathy (DSP) is based on lower intraepithelial nerve fiber density in type 2 prediabetic patients despite normal nerve conduction studies (NCSs). We aimed to confirm the same hypothesis in type 1 diabetic patients by examining whether: (1) subjects without DSP include a spectrum with both normal and abnormal small-fiber measures and (2) subjects with DSP have concurrent evidence of abnormal small-fiber measures. RESEARCH DESIGN AND METHODS: A healthy control population (n = 53) was used to generate threshold values for four small-fiber tests: cooling detection thresholds (CDTs), laser Doppler imaging of heat-evoked flare (LDIflare), heart rate variability (HRV), and corneal confocal microscopy. Based on NCS results, type 1 diabetic patients (n = 131) were dichotomized according to the presence or absence of DSP. RESULTS: Threshold values derived from healthy control subjects were 26.5 °C, 1.4 cm2, 13%, and 12.9 mm/mm2 for CDT, LDIflare, HRV, and corneal nerve fiber length, respectively. Among type 1 diabetic patients, 57 of 131 had evidence of DSP, and 74 of 133 did not. Using abnormality of any small-fiber test to define small-fiber dysfunction, 55 of 57 (96.5%) DSP patients and 39 of 74 (52.7%) control subjects without DSP had concurrent small-fiber damage. The severity of small-fiber abnormalities worsened with an increasing number of NCS abnormalities (ANOVA, P < 0.01). CONCLUSIONS: Our findings in type 1 diabetes support the prevailing hypothesis that small-fiber dysfunction occurs early in DSP. However, further research is required to determine which combination of small-fiber tests is most suitable as a surrogate marker in 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".