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Record W2979570796 · doi:10.1111/bjd.18604

Standardizing the classification of skin tears: validity and reliability testing of the International Skin Tear Advisory Panel Classification System in 44 countries

2019· article· en· W2979570796 on OpenAlex
Hanne Van Tiggelen, Kermit-James E. LeBlanc, M. Karen Campbell, Kevin Woo, Sharon Baranoski, Yee Yee Chang, Ann Marie Dunk, Mary Gloeckner, Heidi Hevia, Samantha Holloway, Patricia Idensohn, Ayişe Karadağ, Einar Sand Koren, Jan Kottner, Diane Langemo, Karen Ousey, Andrea Pokorná, Marco Romanelli, Vera Lúcia Conceição de Gouveia Santos, Steven Smet, Gulnaz Tariq, Karen Van den Bussche, Ann Van Hecke, Sofie Verhaeghe, Hubert Vuagnat, Andrew K. Williams, Dimitri Beeckman

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBritish Journal of Dermatology · 2019
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsQueen's UniversityWestern UniversitySKiN Health
Fundersnot available
KeywordsTearsReliability (semiconductor)MedicineOptometryDermatologyOphthalmologySurgery

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.224

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.064
GPT teacher head0.339
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it