Validation of a New Classification System for Skin Tears
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to validate and establish reliability of the International Skin Tear classification system. METHOD: A consensus panel of 12 internationally recognized key opinion leaders convened in 2011 to establish consensus statements on the prevention, prediction, assessment, and treatment of skin tears. Subsequently, a new skin tear classification system was proposed. The system was then tested for interrater and intrarater reliability between the experts before being tested more widely on a sample of 327 individuals from the United States, Canada, and Europe. RESULTS: The results of the study indicated a substantial level of agreement for the expert panel (Fleiss κ = 0.619; 2-month follow-up = 0.653). Intrarater reliability was high (Cohen κ = 0.877). Interrater reliability was moderate (Fleiss κ = 0.555) for healthcare professionals (n = 303) and fair for non-health professionals (Fleiss κ = 0.338; n = 24). CONCLUSIONS: This international study established the reliability and validity of a new classification system for skin tears.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".