Neuropathic pain in diabetic polyneuropathy: a 5-year prospective study
Bibliographic record
Abstract
ABSTRACT: There are few prospective studies on neuropathic pain in diabetic polyneuropathy (P-DPN). We aimed to examine the development of P-DPN over time as well as factors associated with both the development of and relief from pain. In this 5-year follow-up study, we included 102 patients with at least probable DPN at baseline, according to the Toronto consensus criteria, recruited from a nationwide Danish cohort of 5514 patients with newly diagnosed type 2 diabetes between 2016 and 2018. All participants underwent detailed phenotyping of both DPN and pain, consisting of a bedside sensory examination, quantitative sensory testing (QST), skin biopsies, and nerve conduction studies at baseline and follow-up. The estimated prevalence (95% CI) of at least probable P-DPN increased from 11.5% (8.2; 14.9) at baseline to 14.8% (9.2; 20.4) at follow-up, with a median (interquartile range) diabetes duration of 11.0 (9.2, 12.2) years. Among 64 patients with baseline nonpainful DPN, 38.2% developed pain at follow-up, while 28.9% of 38 patients with baseline P-DPN did not have pain at follow-up. A higher proportion of patients with baseline dysesthesia developed pain (42.9%), compared with patients without dysesthesia (27.9%, Χ2-test for trend: P < 0.0001). Development of pain was associated with female sex, lower baseline sensitivity to warm stimuli on QST, and lower baseline sural sensory nerve action potential amplitudes. Relief from pain was associated with lower baseline body mass index and cholesterol, as well as higher sensitivity to cold, mechanical, and vibratory stimuli on QST at baseline. This detailed study identified risk factors for neuropathic pain development and cessation.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".