The malignant wound assessment tool: a validation study using a Delphi approach
Bibliographic record
Abstract
Malignant wounds, caused by the direct invasion of cancer into the skin, occur in cancer patients with primary skin tumours and as cutaneous metastasis in approximately 10% of patients with metastatic internal malignancies. Malignant wounds have a profound impact on patients, family members and health care providers. The assessment of the patient with malignant wounds can be complex and there is no widely accepted, consistent approach. Valid, descriptive survey research methods were used to develop the Malignant Wound Assessment Tool (MWAT). The authors developed two versions of the MWAT: a brief clinical version (MWAT-C) and a more detailed research version (MWAT-R). Domains include clinical wound features, physical effects and emotional and social impacts of the wound. The two tools underwent content and construct validity testing using a Delphi process. An international panel of professionals with clinical or research expertise related to malignant wounds was formed. Panelists participated in two rounds of review for each tool. Development and face validity testing of the MWAT-C and MWAT-R tools through the Delphi process have resulted in tools ready for clinical application and will support clinical and research activities to improve care for patients with this devastating condition.
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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 | 0.114 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".