Identifying health-related quality of life concepts to inform the development of the WOUND-Q
Bibliographic record
Abstract
OBJECTIVE: The impact of hard-to-heal wounds extends beyond traditional clinical metrics, negatively affecting a patient's health-related quality of life (HRQoL). Yet treatment outcomes are seldom measured from the patient's perspective. The purpose of the present study was to perform in-depth qualitative interviews with patients diagnosed with varying types of hard-to-heal wounds to identify outcomes important to them. METHOD: Participants were recruited from wound care clinics in Canada, Denmark, the Netherlands and the US, and were included if they had a hard-to-heal wound (i.e., lasting ≥3 months), were aged ≥18 years, and fluent in English, Dutch or Danish. Qualitative interviews took place between January 2016 and March 2017. An interpretive description qualitative approach guided the data analysis. Interviews were audio-recorded, transcribed and coded line-by-line. Codes were categorised into top-level domains and themes that formed the final conceptual framework. RESULTS: We performed 60 in-depth interviews with patients with a range of wound types in different anatomic locations that had lasted from three months to 25 years. Participants described outcomes that related to three top-level domains and 13 major themes: wound (characteristics, healing); HRQoL (physical, psychological, social); and treatment (cleaning, compression stocking, debridement, dressing, hyperbaric oxygen, medication, suction device, surgery). CONCLUSION: The conceptual framework developed as part of this study represents the outcome domains that mattered the most to the patients with hard-to-heal wounds. Interview quotes were used to generate items that formed the WOUND-Q scales, a patient-reported outcome measure for patients with hard-to-heal wounds.
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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) | 0.000 | 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".