Effect of model design, cushion construction, and interface pressure mats on interface pressure and immersion
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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) | 0.000 | 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 it