Bibliographic record
Abstract
I am often asked how the International Skin Tear Advisory Panel (ISTAP) came to fruition. During the Toronto World Union of Wound Healing Societies (WUWHS) Congress in 2008, a timely pairing of myself and Sharon Baranoski, MSN, RN, CWCN, APN, MAPWCA, FAAN, as copresenters led to the birth of this organization. Although Payne and Martin1,2 first highlighted the complexity of skin tears in the early 1990s, very little attention was afforded to these wounds between then and 2008. Sharon and I shared a common concern for the unrecognized complexity of skin tear development and the lack of published literature pertaining to these wounds. In 2010, we approached an industry partner for an unrestricted educational grant to conduct an international knowledge, attitude, and practice survey3 of healthcare professionals to gain insight into the extent of the problem, and, as they say, the rest is history. The ISTAP was established in December 2010, and the first meeting was set for early 2011. The domino effect from this modest beginning was beyond our wildest dreams. In 2011, we convened an international panel of 11 like-minded individuals to conduct a Delphi study to establish consensus statements pertaining to the prediction, prevention, assessment, and management of skin tears.4 Original panel members included Dr Karen Campbell (Canada), Dr Kerlyn Carville (Australia), Dawn Christensen (Canada), Karen Edwards (US), Mary Gloeckner (US), Samantha Holloway (UK), Dr Diane Langemo (US), Alicia Madore (US), Mary Ann Sammon (US), Ann Williams (US), and Dr Mary Regan (US); Sharon and I acted as cochairs (Figure 1).Figure 1.: ORIGINAL PANEL MEMBERSSince then, the panel has published more than 15 articles pertaining to skin tears including the initial knowledge, attitude, and practice survey;3 a skin tear consensus document;4 an article on the development and validation of a skin tear classification system;5 a tool kit for the prediction, prevention, assessment, and management of skin tears;6 and recently, best practice recommendations for the prediction, prevention, assessment, and management of these complex wounds.7 In 2016, ISTAP was awarded the WUWHS Most Progressive Society award for its contributions to establishing a robust body of literature concerning skin tears. Recently, a multicountry study validated the content of the ISTAP Classification System (Figure 2) 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 with 1,601 healthcare professionals from 44 countries to measure diagnostic accuracy, agreement, and inter-and intrarater reliability of the instrument. Results were overwhelmingly positive, establishing solid inter- and intrarater reliability of the instrument (Van Tiggelen H, LeBlanc K, Campbell K, et al. “Standardising the Classification of Skin Tears: Validity and Reliability Testing of the International Skin Tear Advisory Panel (ISTAP) Classification System in 44 Countries” [under review]). The ISTAP recommends that clinicians use the instrument as a simple method for classifying skin tears.Figure 2.: SKIN TEAR CLASSIFICATION SYSTEM©2013 International Skin Tear Advisory Panel.Today, the ISTAP includes 19 global expert members from 11 countries. The panel meets virtually 6 times per year and works throughout the year on group projects. The panel has many exciting projects underway, and we are thrilled to be running a workshop on skin tears at the upcoming WUWHS Congress in Abu Dhabi (March 8-12, 2020); ISTAP’s next face-to-face meeting will also be held during the Congress. More information pertaining to ISTAP projects as well as information about membership can be found on our website, www.skintears.org.
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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 | 0.139 | 0.183 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.032 | 0.027 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.043 | 0.061 |
| Insufficient payload (model declined to judge) | 0.025 | 0.013 |
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".