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Record W2214051898 · doi:10.12968/jowc.2015.24.8.388

Danish translation and validation of the International Skin Tear Advisory Panel Skin Tear Classification System

2015· article· en· W2214051898 on OpenAlex

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

VenueJournal of Wound Care · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPressure Ulcer Prevention and Management
Canadian institutionsProfessional Engineers OntarioQueen's University
Fundersnot available
KeywordsMedicineDanishKappaCohen's kappaTest (biology)Health carePhysical therapyMachine learning

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of this study was to translate, validate and establish reliability of the International Skin Tear Classification System in Danish. METHOD: Phase 1 of the project involved the translation of the International Skin Tear Advisory Panel (ISTAP) Skin Tear Classification System into Danish, using the forward-back translation method described by the principles of good practice for the translation process for patient-reported outcomes. In Phase 2, the Danish group sought to replicate the ISTAP validation study and validate the classification system with registered nurses (RN) and social and health-care assistants (non-RN) from both primary health care and a Danish university hospital in Copenhagen. Thirty photographs, with equal representation of the three types of skin tears, were selected to test validity. The photographs chosen were those originally used for internal and external validation by the ISTAP group. The subjects were approached in their place of work and invited to participate in the study and to attend an educational session related to skin tears. RESULTS: The Danish translation of the ISTAP classification system was tested on 270 non-wound specialists. The ISTAP classification system was validated by 241 RNs, and 29 non-RN. The results indicated a moderate level of agreement on classification of skin tears by type (Fleiss' Kappa=0.460). A moderate level of agreement was demonstrated for both the RN group and the non-RN group (Fleiss' Kappa=0.464 and 0.443, respectively). CONCLUSION: The ISTAP Skin Tear Classification System was developed with the goal of establishing a global language for describing and documenting skin tears and to raise the health-care community's awareness of skin tears. The Danish translation of the ISTAP classification system supports the earlier ISTAP study and further validates the classification system. The Danish translation of the classification system is vital to the promotion of skin tears in both research and the clinical settings in Denmark.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.310
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.120
GPT teacher head0.370
Teacher spread0.250 · 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