Volume Measures Using a Digital Image Analysis System are Reliable in Diabetic Foot Ulcers.
Bibliographic record
Abstract
UNLABELLED: Reliable measures of wound size are critical to wound healing research and clinical management. Measurement of full-thickness wounds is increasingly being done using digital images and photogrammetric software, such as VeVMD (Vista Medical, Winnipeg, Manitoba, Canada), to estimate wound volume. The reliability of VeVMD in determining wound volume is unknown. The present study sought to examine the reliability of wound volume measurements obtained using VeVMD. METHODS: A cross-sectional study of adults with full-thickness, neuropathic, diabetic foot ulcers (DFU) at 2 sites in the US Midwest was undertaken. Ulcer images were obtained, stored, and used to obtain measures of wound volume using VeVMD. Four raters independently completed wound measures, and then repeated these measures 2 weeks after the first measurement. Raters were blinded to the comparison measurements. Inter- and intra-rater correlations were computed. RESULTS: Thirty-three enrolled subjects with 33 DFU were included in the analyses. Inter-rater reliability was 0.745 and intra-rater reliability was 0.868. Four ulcers showed noticeably less agreement between raters; these ulcers had small, but deeply recessed areas, resulting in differences in defining the wound margin. When these 4 ulcers were removed, inter- and intra-rater reliabilities were excellent (0.970 and 0.981, respectively). CONCLUSION: Reliabilities of volume measurements obtained with VeVMD were acceptable in DFU, even when raters had different definitions of the ulcer margin or changed their definition from time to time. However, conclusions cannot be drawn regarding the performance of VeVMD in other wound types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".