Development and Validation of the International Skin Tear Advisory Panel Skin Tear Data Collection Tool
Bibliographic record
Abstract
OBJECTIVE: Numerous studies have examined the epidemiology of skin tears; however, inconsistent definitions, classification systems, and data collection methods have highlighted the need for a validated and standardized tool. The primary objective of this study was to validate a data collection tool for skin tears. A secondary aim was to provide a freely accessible tool for health care providers or researchers to collect consistent and reliable data on skin tears. METHODS: The development of the tool was guided by the 2018 International Skin Tear Advisory Panel (ISTAP) Best Practice Recommendations for the prevention, assessment, and management of skin tears in aged skin. Between June and October 2024, a multimethod validation process was undertaken. Content validity ratio and content validity index calculations were used to quantify content validity, supported by qualitative feedback from 15 experts to assess face validity and provide suggestions for refinement. RESULTS: The final tool consists of 22 questions addressing a patient's demographics, clinical features of the skin tear, associated risk factors, and contextual data. The content validity index was calculated at 0.72, indicating an acceptable level of content validity. International experts reached consensus following a 2-round qualitative review, resulting in subsequent adjustments to the tool. CONCLUSIONS: The ISTAP DC-Tool was developed based on evidence-informed recommendations and validated by an international panel of experts. Its implementation will support health care providers and researchers in gathering standardized epidemiological data contributing to clinical practice improvements, quality initiatives, and further research in skin tear prevention and management.
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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 | 0.168 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".