Small and large fiber sensory polyneuropathy in type 2 diabetes: Influence of diagnostic criteria on neuropathy subtypes
Bibliographic record
Abstract
Diabetic polyneuropathy (DPN) can be classified based on fiber diameter into three subtypes: small fiber neuropathy (SFN), large fiber neuropathy (LFN), and mixed fiber neuropathy (MFN). We examined the effect of different diagnostic models on the frequency of polyneuropathy subtypes in type 2 diabetes patients with DPN. This study was based on patients from the Danish Center for Strategic Research in Type 2 Diabetes cohort. We defined DPN as probable or definite DPN according to the Toronto Consensus Criteria. DPN was then subtyped according to four distinct diagnostic models. A total of 277 diabetes patients (214 with DPN and 63 with no DPN) were included in the study. We found a considerable variation in polyneuropathy subtypes by applying different diagnostic models independent of the degree of certainty of DPN diagnosis. For probable and definite DPN, the frequency of subtypes across diagnostic models varied from: 1.4% to 13.1% for SFN, 9.3% to 21.5% for LFN, 51.4% to 83.2% for MFN, and 0.5% to 14.5% for non-classifiable neuropathy (NCN). For the definite DPN group, the frequency of subtypes varied from: 1.6% to 13.5% for SFN, 5.6% to 20.6% for LFN, 61.9% to 89.7% for MFN, and 0.0% to 6.3% for NCN. The frequency of polyneuropathy subtypes depends on the type and number of criteria applied in a diagnostic model. Future consensus criteria should clearly define sensory functions to be tested, methods of testing, and how findings should be interpreted for both clinical practice and research purpose.
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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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".