Paediatric pressure injuries – a review and recommendations for hospital providers
Bibliographic record
Abstract
Objectives: Paediatric pressure injuries (PIs) are a serious constellation of wounds that can lead to additional suffering, lifelong scarring, increased risk of infection, and high costs to the healthcare system. The prevalence of paediatric PI in an inpatient setting is 1.4% and can be as high as 43% in critical care units. The most common causes of paediatric PIs are associated with pressure from prolonged immobility and medical devices. Methods: A narrative literature review was conducted to survey the current state of paediatric PI management for the purpose of providing healthcare providers with updated insight into PI management. Results: Compared to the adult population, there are unique differences in paediatric anatomy and physiology depending on age and weight that can affect the aetiology and location of PIs. There has also been a development of tools to assess paediatric PIs. Prompt risk assessment within 8 hours following admission with a structured risk assessment tool, such as the Braden QD, followed by thorough skin assessments at regularly spaced intervals, will aid in the detection and treatment of PIs. The optimization of skin health and the use of medical devices are also key to the prevention of PIs. Conclusion: This article reviews the unique differences of PIs in children and provides recommendations on prevention, care, and treatment.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".