Development of prone positioning and skin damage prevention digital education: the PRONEtect project
Bibliographic record
Abstract
OBJECTIVE: The incidence of skin/tissue damage, such as pressure ulcers, remains high in mechanically ventilated patients in the prone position. According to guidelines, critically ill patients with acute respiratory distress syndrome (ARDS) should be prone for at least 12-16 hours to improve oxygenation and decrease mortality. Therefore, educating clinicians on how to reposition and manage the patient safely in a prone position plays a vital role in preventing adverse events. This project aimed to develop accessible online educational content to assist clinicians in safely executing the prone manoeuvre and minimise skin/tissue damage. METHOD: The development of the educational content was based on: a gap analysis and comprehensive review of available educational resources; evidence-based scientific literature; advice from international experts; and a qualitative study exploring the learning needs of 20 clinicians in Belgium and Sweden between February-August 2022. RESULTS: Volunteer clinicians assisted with the creation of eight simulation videos which were professionally filmed and edited. The interactive videos included the supine-to-prone and prone-to-supine manoeuvres, endotracheal and nasogastric tube securement, eye care, stoma care, protecting high-risk areas from pressure damage, and incontinence-associated dermatitis prevention. A prone positioning protocol, a checklist summarising the key aspects of the protocol, and teaching aids (slide deck for didactic lecturing) were developed and validated by a review of the relevant evidence-based literature and the international expert panel. A website was designed to host the content, with free user access, at www.pronetection.com. CONCLUSION: Education is one strategy towards prevention of complications of prone positioning. Accessible education could assist clinicians unfamiliar with prone positioning or current clinicians requiring refresher training to safely manage patients in this position.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".