Assessing chronic wound perfusion in the lower extremity: current and emerging approaches
Bibliographic record
Abstract
Kevin Y Woo,1 Timothy M Brandys,2 Joseph A Marin1 1School of Nursing, Faculty of Health Sciences, Queen's University, Kingston, 2Department of Surgery, The Ottawa Hospital, Ottawa, ON, Canada Abstract: Current evidence suggests that vascular and wound bed perfusion assessment should be integral to the care of people with chronic wounds in the lower extremities. Peripheral artery diseases can be insidious, with many affected individuals unaware of circulation issues and failing to seek medical help until they develop foot ulcers, gangrene, and other related complications. Measuring tissue perfusion is a useful diagnostic marker for chronic wounds and may help determine a wound's potential for healing. A complete patient history and physical examination is imperative to help determine the correct diagnosis of lower extremity ulcers. This article aims to discuss current and emerging wound perfusion assessment techniques, including ankle–brachial index, toe–brachial index, transcutaneous oxygen pressure, near-infrared spectroscopy, digital subtraction angiography, magnetic resonance angiography, computed tomographic angiography, and indocyanine green angiography. Keywords: tissue perfusion, peripheral arterial disease, oxygen, wound healing
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".