Can Wound Exudate from Venous Leg Ulcers Measure Wound Pain Status?: A Pilot Study
Bibliographic record
Abstract
We investigated the associations between the self-evaluated pain status and two pain biomarker candidates, nerve growth factor and S100A8/A9, in exudate from venous leg ulcer to finally develop an objective pain evaluation method. Patients with venous leg ulcer participated in this cross-sectional observational study conducted between April and October 2014 at two medical facilities. During routine wound care, each participant self-evaluated their pain status at each examination using the 10-point numerical rating scale (present pain intensity) and the short-form McGill Pain Questionnaire 2 (continuous pain, intermittent pain, neuropathic pain, affective descriptors, and total score). Venous leg ulcer exudate sample was collected after wound cleansing. The nerve growth factor and S100A8/A9 concentrations in the venous leg ulcer exudate were measured by enzyme-linked immunosorbent assay and standardized according to the wound area. The association between each pain status and the two standardized protein concentrations was evaluated using Spearman's correlation coefficient. In 30 sample collected from 13 participants, the standardized nerve growth factor concentration was negatively correlated with continuous pain (ρ = -0.47, P = 0.01), intermittent pain (ρ = -0.48, P = 0.01), neuropathic pain (ρ = -0.51, P = 0.01), and total score (ρ = -0.46, P = 0.01). The standardized S100A8/A9 concentration was positively correlated with present pain intensity (ρ = 0.46, P = 0.03) and continuous pain (ρ = 0.48, P = 0.03). Thus, these two proteins may be useful for objective evaluation of wound pain in venous leg ulcer patients.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".