Further psychometric validation and test–retest reproducibility of the <scp>WOUND‐Q</scp>
Bibliographic record
Abstract
WOUND-Q is a condition-specific patient-reported outcome measure developed for all types of chronic wounds, located anywhere on the body. To establish reliability and validity of a patient-reported outcome measure, multiple pieces of evidence are required. The purpose of this study was to examine the measurement properties of 9 of the 13 WOUND-Q scales and perform a test-retest reproducibility study in an international sample. In August 2022, we invited members of an international online community (Prolific.com) with any type of chronic wound to complete a survey containing the WOUND-Q scales, the Wound-QoL and EQ-5D. A test-retest survey was performed 7 days after the first survey. It was possible to examine the reliability and validity of eight of the nine WOUND-Q scales by Rasch Measurement Theory (RMT). To examine test-retest reproducibility intraclass correlation coefficients (ICCs), the standard error of the measurement and the smallest detectable change were calculated. In total, 421 patients from 22 different countries with 11 different types of chronic wounds took part in this study. Our analyses provided further evidence of the reliability and validity of the scales measuring wound characteristics (assessment, drainage, smell), health-related quality of life (life impact, psychological, sleep, social) and wound treatment (dressing).
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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 | 0.111 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".