Standardizing the classification of skin tears: validity and reliability testing of the International Skin Tear Advisory Panel Classification System in 44 countries
Bibliographic record
Abstract
BACKGROUND: Skin tears are acute wounds that are frequently misdiagnosed and under-reported. A standardized and globally adopted skin tear classification system with supporting evidence for diagnostic validity and reliability is required to allow assessment and reporting in a consistent way. OBJECTIVES: To measure the validity and reliability of the International Skin Tear Advisory Panel (ISTAP) Classification System internationally. METHODS: A multicountry study was set up to validate the content of the ISTAP Classification System through expert consultation in a two-round Delphi procedure involving 17 experts from 11 countries. An online survey including 24 skin tear photographs was conducted in a convenience sample of 1601 healthcare professionals from 44 countries to measure diagnostic accuracy, agreement, inter-rater reliability and intrarater reliability of the instrument. RESULTS: A definition for the concept of a 'skin flap' in the area of skin tears was developed and added to the initial ISTAP Classification System consisting of three skin tear types. The overall agreement with the reference standard was 0·79 [95% confidence interval (CI) 0·79-0·80] and sensitivity ranged from 0·74 (95% CI 0·73-0·75) to 0·88 (95% CI 0·87-0·88). The inter-rater reliability was 0·57 (95% CI 0·57-0·57). The Cohen's Kappa measuring intrarater reliability was 0·74 (95% CI 0·73-0·75). CONCLUSIONS: The ISTAP Classification System is supported by evidence for validity and reliability. The ISTAP Classification System should be used for systematic assessment and reporting of skin tears in clinical practice and research globally. What's already known about this topic? Skin tears are common acute wounds that are misdiagnosed and under-reported too often. A skin tear classification system is needed to standardize documentation and description for clinical practice, audit and research. What does this study add? The International Skin Tear Advisory Panel Classification System was psychometrically tested in 1601 healthcare professionals from 44 countries. Diagnostic accuracy was high when differentiating between type 1, 2 and 3 skin tears using a set of validated photographs.
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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 | 0.108 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".