Wound Healing Assessment
Bibliographic record
Abstract
INTRODUCTION: Studies on dressings frequently measure wound healing to demonstrate performance. Knowledge of existing methodologies available for wound healing assessment, including their advantages and limitations, is paramount when evaluating the literature on dressings. METHODOLOGY: Medline and Cochrane databases were searched for wound healing assessment methodologies used in research or in clinical practice. RESULTS: Twenty-nine methodologies were identified and classified into 8 categories: scales (n = 4), one-dimensional measurements (n = 2), area measurements (n = 4), volume measurements (n = 6), 3-dimensional wound reproduction systems (n = 5), methodologies based on wound physical characteristics (n = 3), rates and surrogates end point calculated from variation in wound dimensions (n = 4), and time to wound healing (n = 1). The main problems encountered during wound healing assessment include the following: boundary definition, assessor's contact with the wound, irregular wound shape, and difficulty in evaluating early healing. CONCLUSION: At this time, an ideal methodology does not exist. Research in this area is lacking and should be the focus in wound healing evaluation.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.005 |
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".