Reliability and measurement error of digital planimetry for the measurement of chronic venous leg ulcers
Bibliographic record
Abstract
Abstract Area measurements of a chronic wound are the gold standard outcome measure to determine if a wound is on a healing or nonhealing trajectory. The use of digital planimetry can provide increased accuracy in measuring wound area however it is important to know the reliability and measurement error of these devices when used by multiple assessors. The aim of this study is to determine the within rater, between rater, and standard error of measurement of a digital planimetry device. Wound area in 42 patients was measured weekly for 12 weeks by two different raters, with each rater measuring the wound 10 times per visit. Intraclass correlation coefficients (ICC 1,k) and standard error of measurement were calculated for both within and between raters using 10 and the first three repeated measures to determine if using less measurements was as reliable. The true change in wound area was calculated by dividing stander error of measurements by mean wound areas. Within rater reliability for raters 1 and 2 were 0.995 and 0.992 for 10 measurements, and 0.996 and 0.992 for 3 measurements per time point. Between rater reliability was 0.979 for 10 measurements and 0.996 for 3 measurements per time point. The within rater standard error of measurement for raters 1 and 2 was 0.98 cm 2 and 1.28 cm 2 for 10 measurements and 0.895 cm 2 and 1.29 cm 2 for 3 measurements at each time point. The standard error of measurement for between raters was 2.07 cm 2 for 10 measurements and 2.25 cm 2 for 3 measurements per time point. The true change in wound size varied from 6.4% for within one rater to 15.7% for across different raters. This study found that both within and between rater reliability of the digital planimetry device was very high for three measurements per time point.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".