PP431 Topic: AS15–Lung: Respiratory Support/Acute Respiratory Failure/Other: A REVIEW OF PRESSURE INJURIES FROM THE USE OF NON-INVASIVE VENTILATION IN THE PICU
Bibliographic record
Abstract
Aims & Objectives: Non-invasive ventilation (NIV) is used to treat acute and chronic respiratory conditions in the PICU. NIV use is rapidly increasing in the Pediatric Intensive Care Unit (PICU) and is delivered through various interface types. Pressure injuries (PI) are a complication of NIV, with up to 31% of all PI’s attributed to NIV interfaces. Severe PIs come with significant sequelae including the need for intubation/invasive ventilation and increased mortality. Other consequences include: infection, plastic surgery, and longer stays. We aimed to describe what is known about PI/NIV in the PICU. Methods: Utilizing a librarian, we reviewed literature from 2009-2023 searching keywords: Non-invasive; Pressure Ulcer Risk Factors, Etiology; Prevention and Control; Respiration; Intensive Care Unit Equipment and Supplies Adverse Effects. We selected the 20 most relevant papers. Results: Key themes included: 1) A correlation between low or high leak and increasing PI. 2) The importance of off-loading in the prevention of PI: allowing pressure relief, face and interface cleaning. 3) Mannequin studies conflict with those of live patient studies. In live models, dressings proved beneficial, in the mannequin model, dressings demonstrated points of increased pressure and leak. 4) 3D printing may be beneficial by allowing customization of the interface to the patient’s face. Conclusions: Although mannequins may be beneficial for initial testing, it is unable to mimic all factors of a real patient’s skin: hydration, condensation, movement, etc. thus future research must include clinical studies on live patients. Upcoming technologies such as 3D printing are an exciting prospect. Keywords: non-invasive ventilation, PICU, Pressure Injury
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".