Validating the Italian Version of the International Skin Tear Advisory Panel Classification System
Bibliographic record
Abstract
OBJECTIVE: To validate the International Skin Tear Advisory Panel (ISTAP) Classification System in Italian. METHODS: In collaboration with the ISTAP, the classification system was translated into Italian using a forward-back translation process. To validate the translated system, a convenience sample of 212 health professionals classified 30 photographs of skin tears originally used by ISTAP. The wound images were labeled type 1, 2, or 3 as described by the classification system. The resulting scores were compared with the ISTAP classification, and the reliability of agreement was calculated with Fleiss κ. RESULTS: Complete data were obtained from 209 healthcare professionals. When the image classifications were compared with the original ISTAP indications, 72.5% of all classifications were correct. Data indicated a moderate level of agreement (Fleiss κ = 0.466, range = 0.41-0.60). Data analysis showed similar agreement levels between nurses (n = 197, Fleiss κ = 0.466) and nonnurses (n = 12, Fleiss κ = 0.46). CONCLUSIONS: The study validates the Italian version of the ISTAP skin tear classification system. Further studies are necessary to confirm the system's usability in Italian research and clinical settings.
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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 | 0.033 | 0.058 |
| 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 | 0.001 | 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".