Development, Validity, Reliability, and Responsiveness of a New Leg Ulcer Measurement Tool
Bibliographic record
Abstract
OBJECTIVE: To develop and validate an assessment tool--the Leg Ulcer Measurement Tool (LUMT)--that would be able to detect changes in the appearance of lower extremity ulcers. SUBJECTS: Twenty-two subjects with chronic leg ulcers of various etiologies (arterial, venous, diabetes) participated in the validation study. DESIGN: An interdisciplinary panel consisting of 9 local wound care specialists confirmed content validity. Concurrent criterion validity was determined by correlation of the size domain (1 of 14 clinician-rated domains in the LUMT) with acetate tracing measurement of wound surface area. Reliability was determined using repeated assessments by 4 wound care specialist and 2 inexperienced evaluators; responsiveness was determined using monthly reassessments by a single rater for 4 months. RESULTS: Concurrent criterion validity was r = 0.82. Excellent values of intrarater and interrater reliability (ICC > 0.75) were obtained for total LUMT scores and for many of the 14 individual domains; however, several domains were found to be less reproducible. The LUMT detected change in wound status over time (responsiveness coefficient = 0.84). CONCLUSION: The LUMT can be used by 1 or more assessors, with relatively little previous training, to make reproducible evaluations of lower extremity ulcer appearance and to document change in appearance over time. The LUMT represents a novel assessment tool specifically designed and validated for clinical or research use on chronic leg ulcers.
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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 | 0.023 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".