Diagnosis and prevalence of diabetic polyneuropathy: a cross‐sectional study of Danish patients with type 2 diabetes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Diabetic polyneuropathy (DPN) is a common complication of diabetes. Using the Toronto criteria for diabetic polyneuropathy and the grading system for neuropathic pain, the performance of neuropathy scales and questionnaires were assessed by comparing them to a clinical gold standard diagnosis of DPN and painful DPN in a cohort of patients with recently diagnosed type 2 diabetes. METHODS: A questionnaire on neuropathy and pain was sent to a cohort of 5514 Danish type 2 diabetes patients. A sample of 389 patients underwent a detailed clinical examination and completed neuropathy questionnaires and scales. RESULTS: Of the 389 patients with a median diabetes duration of 5.9 years, 126 had definite DPN (including 53 with painful DPN), 88 had probable DPN and 53 had possible DPN. There were 49 patients with other causes of polyneuropathy, neuropathy symptoms or pain, 10 with subclinical DPN and 63 without DPN. The sensitivity of the Michigan Neuropathy Screening Instrument questionnaire to detect DPN was 25.7% and the specificity 84.6%. The sensitivity of the Toronto Clinical Neuropathy Scoring System, including questionnaire and clinical examination, was 62.9% and the specificity was 74.6%. CONCLUSIONS: Diabetic polyneuropathy affects approximately one in five Danish patients with recently diagnosed type 2 diabetes but neuropathic pain is not as common as previously reported. Neuropathy scales with clinical examination perform better compared with questionnaires alone, but better scales are needed for future epidemiological studies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".