Chronic painful peripheral neuropathy in an urban community: a controlled comparison of people with and without diabetes
Bibliographic record
Abstract
AIMS: A cross-sectional study has been performed in order to estimate the prevalence, severity, and current treatment of chronic painful peripheral neuropathy (CPPN) in people with diabetes in the community. METHODS: Using a structured questionnaire and examination we have assessed these factors in a community sample of people with diabetes (n=350) and compared them with 344 age- and sex-matched people without diabetes from the same locality. RESULTS: The prevalence of CPPN was estimated to be 16.2%[95% confidence interval (CI): 6.8-16%] in people with diabetes compared with 4.9% (95% CI: 2.6-7.2%) in the control sample (P < 0.0001). Diabetic subjects with and without CPPN did not differ in age, sex, type and duration of diabetes, body mass index, smoking status and glycaemic control. However, CPPN diabetic subjects had significantly higher Visual Analogue Scale (VAS) scores for pain over the preceding 24 h [median (interquartile range) 3.5 (1.5-6.7) cm vs. 0.7 (0-3.9) cm, P < 0.0001]. Also, the total McGill Pain Questionnaire Score (a measure of pain quality and severity) was 18 (13-31.5) vs. 10 (4-16) (P < 0.0001). Of patients with diabetes and CPPN, 12.5% (7/56) had never reported their symptoms to their treating physician and 39.3% (22/56) had never received any treatment for their painful symptoms. CONCLUSIONS: CPPN is common, often severe but frequently unreported and inadequately treated.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".