A Study of the Utility and Equivalency of 2 Methods of Wound Measurement
Bibliographic record
Abstract
OBJECTIVE: To examine agreement between digitized tracing and digital photography methods in measuring wound area and healing rate, and to compare and contrast the 2 methods on feasibility and utility in patient care and research settings. SETTING: Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada. PARTICIPANTS: A total of 20 subjects aged 18 years or older with a spinal cord injury and pressure ulcers that were Stage II or higher, and who had received in- or outpatient wound care at the hospital for at least 3 consecutive weeks. METHODS: Wound area was measured at weekly intervals. One assessor calculated wound area from a digitized tracing. A second assessor calculated wound area using a wound photograph. Both assessors used Image-J software. The 2 methods were compared for differences in weekly wound area and weekly healing rate. RESULTS: Methods were different for wound area (P < .0001), whereas there was no difference between methods in weekly healing rate (P = .9429). CONCLUSIONS: The 2 methods are in agreement on the important parameter of healing rate. Both methods are feasible in clinical settings. Wound photography may be more useful than digitized tracings because it simultaneously captures wound appearance.
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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 | 0.071 | 0.250 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".