Effect of model design, cushion construction, and interface pressure mats on interface pressure and immersion
Bibliographic record
Abstract
Measuring interface pressure (IP) is one way to characterize cushion performance in the clinic and laboratory. This study explored how the presence of four commercially available IP mats affected IP on and immersion of two buttocks models. We loaded seven cushions with each buttocks model and captured pressure data using FSA sensors (Vista Medical Ltd; Winnipeg, Manitoba, Canada). Analysis was performed to compare pressure magnitude and immersion. Overall, both pressure magnitude and immersion changed after mat introduction. A significant interaction existed between cushion and mat condition and cushion and model for all variables. Introducing an IP mat to the model-cushion interface alters the loading on the cushion. The mats bridged the contours of the model, causing a change in IP at the locations studied. Although immersion was statistically different between mat conditions, the magnitude of the difference was less than 1 mm once we accounted for the thickness of the mats. The significance of the cushion-mat interaction indicates that the mat effect differed across cushion design. Clinical and research users of pressure mats should consider the effect of mat presence, the effect of model design, and mat and buttocks interactions with cushions for successful use.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".