Evaluation of wound fluid biomarkers to determine healing in adults with venous leg ulcers: A prospective study
Bibliographic record
Abstract
Clinical practice guidelines recommend using repeated wound surface area measurements to determine if a chronic ulcer is healing. This results in delays in determining the healing status. This study aimed to evaluate whether any of a panel of biomarkers can determine the healing status of chronic venous leg ulcers. Forty-two patients with chronic venous leg ulcers had their wound measured and wound fluid collected at weekly time points for 13 weeks. Wound fluid was analyzed using multiplex enzyme-linked immunosorbent assay to determine the concentration of biomarkers in the wound fluid at each weekly time point. Healing status was determined by examining the change in wound size at the previous and subsequent weeks. Predictive accuracy with 95% confidence intervals (CI) is reported. Of 42 patients, 105 evaluable weekly time points were obtained, with 32 classified as healing, 27 as nonhealing, and 46 as indeterminate. Thirteen biomarkers significantly differed between healing and nonhealing wounds (p < 0.1) and were included in a multivariate logistic regression model. Granulocyte macrophage-colony stimulating factor (p < 0.001) and matrix metalloprotease-13 (p = 0.004) were the best predictors of wound healing. Receiver operating characteristic curves indicated 92% accuracy (95% CI: 85%,100%) for granulocyte macrophage-colony stimulating factor, and 78% accuracy (95% CI: 65%,90%) for matrix metalloprotease-13 in discriminating between healing and nonhealing wounds. This study found that two biomarkers from wound fluid can predict healing status in chronic venous leg ulcers. These findings may lead to the ability to determine the future trajectory of a wound and the ability to modify treatment accordingly.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".