The Prevalence, Severity, and Impact of Painful Diabetic Peripheral Neuropathy in Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To determine the prevalence of painful diabetic peripheral neuropathy (PDPN) in a population-based sample and to estimate its severity and impact. RESEARCH DESIGN AND METHODS: A cross-sectional descriptive study consisting of two phases: phase 1, a postal survey to patients with type 2 diabetes (an initial screening questionnaire including one question about pain); phase 2, neurological history and examination using the Toronto Clinical Scoring System. Subjects with PDPN or mixed (PDPN and nonneuropathic) pain completed the Neuropathic Pain Scale and Neuroqol to assess severity and nature of the pain and impact on quality of life. Those without PDPN completed the Neuroqol only. RESULTS: In phase 1, there was a 92.7% response (n = 326), with 208 (63.8%) subjects reporting pain. In phase 2, 269 (82.5%) subjects attended and 51 (19.0%) were found to have PDPN: 99 (36.8%) nonneuropathic pain, 20 (7.4%) mixed pain, and 99 (36.8%) no pain (PDPN prevalence 26.4%). Of those with PDPN, 80% stated that their pain was moderate or severe. Those affected had poorer quality of life than those with no pain (difference in mean scores 3.6 [95% CI 2.5-4.6%]) compared with those with nonneuropathic pain (1.7 [0.4-2.9%]). Both pain and neuropathy score were independently associated with quality of life, and subjects with PDPN had significantly higher neuropathy scores. CONCLUSIONS: Our study showed a prevalence of PDPN of 26.4%. Having PDPN has a significant negative effect on quality of life, and increasing neuropathy is associated with an increasing risk of developing PDPN.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".