Danish translation and validation of the International Skin Tear Advisory Panel Skin Tear Classification System
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to translate, validate and establish reliability of the International Skin Tear Classification System in Danish. METHOD: Phase 1 of the project involved the translation of the International Skin Tear Advisory Panel (ISTAP) Skin Tear Classification System into Danish, using the forward-back translation method described by the principles of good practice for the translation process for patient-reported outcomes. In Phase 2, the Danish group sought to replicate the ISTAP validation study and validate the classification system with registered nurses (RN) and social and health-care assistants (non-RN) from both primary health care and a Danish university hospital in Copenhagen. Thirty photographs, with equal representation of the three types of skin tears, were selected to test validity. The photographs chosen were those originally used for internal and external validation by the ISTAP group. The subjects were approached in their place of work and invited to participate in the study and to attend an educational session related to skin tears. RESULTS: The Danish translation of the ISTAP classification system was tested on 270 non-wound specialists. The ISTAP classification system was validated by 241 RNs, and 29 non-RN. The results indicated a moderate level of agreement on classification of skin tears by type (Fleiss' Kappa=0.460). A moderate level of agreement was demonstrated for both the RN group and the non-RN group (Fleiss' Kappa=0.464 and 0.443, respectively). CONCLUSION: The ISTAP Skin Tear Classification System was developed with the goal of establishing a global language for describing and documenting skin tears and to raise the health-care community's awareness of skin tears. The Danish translation of the ISTAP classification system supports the earlier ISTAP study and further validates the classification system. The Danish translation of the classification system is vital to the promotion of skin tears in both research and the clinical settings in Denmark.
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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 | 0.026 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".