ISTAP classification for skin tears: Validation for Brazilian Portuguese
Bibliographic record
Abstract
The objective of this study was to evaluate interobserver reliability and the concurrent criterion validity of the adapted version of the International Skin Tear Advisory Panel (ISTAP) Skin Tear Classification System to Brazilian Portuguese. For the evaluation of interobserver reliability using the photograph database, 36 nurses classified 30 skin tears (STs) into three groups, according to its definitions (adapted version). For the evaluation through clinical application, 23 nurses classified 12 STs present in 8 thoracic and cardiovascular postoperative patients at a tertiary hospital in Sao Paulo, Brazil. For the data collection of patients, an enterostomal therapist nurse classified the ST found by simultaneously using the adapted ISTAP version and the Skin Tear Audit Research (STAR) Classification System to test the concurrent criterion validity. The average of 17.83 correct answers (SD = 5.03) resulted from 1080 photograph observations, with Fleiss κ = 0.279 (reasonable concordance level). The interobserver reliability in the clinical application resulted in a global correct answer percentage of 76.7% in 85 observations. The concurrent criterion validity was attested by the total correlation (r = 1) between ISTAP and STAR. The ISTAP classification for ST is a reliable instrument and also valid in Brazil, making it another option to be used in clinical practice.
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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".