Swedish translation and validation of the international skin tear advisory panel skin tear classification system
Bibliographic record
Abstract
The aims of this study were to translate the International Skin Tear Advisory Panel (ISTAP) classification system for skin tears into Swedish and to validate the translated system. The research process consisted of two phases. Phase I involved the translation of the classification system, using the forward-back translation method, and a consensus survey. The survey dictated that the best Swedish translation for "skin tear" was "hudfliksskada." In Phase 2, the classification system was validated by health care professionals attending a wound care conference held in the spring of 2017 in Sweden. Thirty photographs representing three types of skin tear were presented to participants in random order. Participants were directed to classify the skin tear types in a data collection sheet. The results indicated a moderate level of agreement on classification of skin tears by type. Achieving moderate agreement for the ISTAP skin tear tool is an important milestone as it demonstrates the validity and reliability of the tool. Skin tear classification typing is a complex skill that requires training and time to develop. More education is required for all health care specialists on the classification of skin tears.
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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 | 0.039 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".