In Vitro Characterization of Pressure Redistribution Among Commercially Available Wound Dressings
Bibliographic record
Abstract
OBJECTIVE: Recent clinical evidence has suggested that certain wound dressings may play a significant role in protocols to prevent or reduce pressure injury (PI) in patients at risk by modifying the pressure, friction, and shear forces that can contribute to PI. The aim of this study was to investigate the pressure reduction properties of commercially available wound dressings in vitro. METHODS: Using a standardized protocol (1.7 kg, 7.5-cm sphere), testing was performed in a controlled environment by the same clinician using a pressure mapping device (XSENSOR LX205; XSENSOR Technology Corporation, Calgary, Alberta, Canada) to measure and compare the pressure mitigation properties in a variety of wound dressings. RESULTS: A total of 13 different commercially available dressings were tested in triplicate for changes in pressure redistribution as compared with the control. One dressing demonstrated the greatest reduction of pressure forces (OxyBand PR; 50.33 ± 1.45 mm Hg) compared with the control (302.7 ± 0.33 mm Hg) and the greatest surface area of all the study dressings tested. There was a negative correlation (R2 = 0.73) between the average pressure distribution of a wound dressing and its contact area. Further, the peak pressure for OxyBand PR (P ≤ .05) was significantly different from all other tested dressings. CONCLUSIONS: One dressing (OxyBand PR) provided superior pressure redistribution and significantly reduced peak pressure in this study when compared with currently available standard foam and silicone dressings that are marketed for the purpose of PI prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".