Evaluation of neuropathic pain in lower extremity wounds using different assessment tools: A cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: Patients with lower extremity wounds often experience neuropathic pain; however, there is no validated assessment tool to specifically measure wound-related neuropathic pain. The study aimed to assess the prevalence of neuropathic pain in lower extremity wounds using different assessment tools and to identify factors associated with neuropathic pain. METHODS: A cross-sectional study of 130 patients with lower extremity wounds of different etiologies assessed neuropathic pain through clinical examinations, the Short Form McGill Pain Questionnaire-2 (SF-MPQ-2), and the Douleur Neuropathique 4 Questions (DN4). Pain intensity was measured using the Visual Analog Scale (VAS). RESULTS: In total, 38 (29%) experienced neuropathic pain (DN4 score ≥ 4), and 75% (n = 97) described pain using one or more neuropathic pain descriptors on the SF-MPQ-2. The frequently reported descriptors on the neuropathic sub-scale were "pain caused by light touch" (59%) and "tingling or pins and needles" (49%). There was a positive correlation between DN4 and the neuropathic sub-scale of SF-MPQ-2, and the major difference between the tools is the design and time consumption. Univariate analysis revealed that younger age, arterial wound type, infection, and morphine consumption were associated with neuropathic pain (DN4 score ≥ 4). In multivariate analysis, arterial wound type increased the risk of neuropathic pain five-fold. Younger age and morphine consumption were also significantly associated with neuropathic pain, whereas infection was not. CONCLUSION: Neuropathic wound pain is frequent, and the prevalence relies on the applied assessment tool. Arterial wound type, younger age, and morphine consumption are associated with neuropathic wound pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.055 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".