Bibliographic record
Abstract
summarises the main points they presented at the EWMA 2020 Virtual Conference This article offers an introduction to the symposium, Skin integrity-the perpetual challenge, held on 18 November 2020, as part of the EWMA 2020 Virtual Conference. There were three speakers. Dimitri Beeckman, Professor of Skin Integrity and Clinical Nursing, Ghent University, Belgium, focused on moisture lesions or MASD (moisture-associated skin damage). Karen Campbell, Consultant, Primacare Living Solutions and Adjunct Professor, MClScWH, Western University, London, Ontario, Canada, focused on the concepts related to skin vulnerability. She aimed to identify shared risk factors for skin conditions and ways to promote skin integrity, formulating a synergistic prevention approach to break down barriers in practice. The third speaker was Alessandro Corsi, Wound Care Consultant and Surgeon, Director of Wound Care Unit, IRCCS San Raffaele Hospital, Milan. He looked in detail at the dressings available in this area, detailing how he and his team had successfully used the Essity line of Skin Sensitive silicone dressings in their hospital.
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 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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".