International study to develop the <scp>WOUND‐Q</scp> patient‐reported outcome measure for all types of chronic wounds
Bibliographic record
Abstract
Patient-reported outcome measures (PROMs) for chronic wounds mainly focus on specific types of wounds. Our team developed the WOUND-Q for use with all types of wounds in any anatomic location. We conducted 60 concept elicitation interviews with patients in Canada, Denmark, the Netherlands, and the United States. Analysis identified concepts of interest to patients and scales were formed and refined through cognitive interviews with 20 patients and input from 26 wound care experts. Scales were translated into Danish and Dutch. An international field-test study collected data from 881 patients (1020 assessments) with chronic wounds. Rasch measurement theory (RMT) analysis was used to refine the scales and examine psychometric properties. RMT analysis supported the reliability and validity of 13 WOUND-Q scales that measure wound characteristics (assessment, discharge, and smell), health-related quality of life (life impact, psychological, sleep impact, and social), experience of care (information, home care nurses, medical team, and office staff), and wound treatment (dressing and suction device). The WOUND-Q can be used to measure outcomes in research and clinical practice from the perspective of patients with any type of wound.
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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 | 0.029 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".