Pressure Distribution Comparison among Standard Seating Surfaces and Strap Seating System
Bibliographic record
Abstract
AIMS: Pressure injuries (PIs) are common issues that can be minimized through the use of pressure-redistributing support surfaces. Cushions that provide immersion and contour are considered the most effective for pressure relief; however, others are readily available on the market. The aim of this study is to determine how a wheelchair equipped with Comfort Tension Seating®(CTS) compares to standard sling seating, foam, and a high-end pressure redistributing contoured cushion. MATERIALS & METHODS: Pressure redistribution qualities as measured through peak pressure index (PPI) using pressure mapping technology were gathered on four different seating surfaces -standard sling seat, CTS, and two cushion types flat cross-section foam, contoured-cushion, and CTS. Twenty non-disabled participants trialed each cushion for five minutes each. The methods of this study are described and outcomes analyzed by comparing the PPI and comfort of the four cushions. RESULTS: A Wilcoxon signed-rank test, and related samples Friedman’s two-way analysis of variance by ranks (ANOVA) was calculated. The results show that there is a significant difference between each of the cushions in comfort and pressure redistribution. There was a statistically significant difference in mean PPI between the three groups in which the CTS performed better than the sling and flat cross-section foam, but not quite as good as the high-end contoured cushion (p <.001). CONCLUSION: While not as optimal as the contoured M2 foam cushion, the CTS seating surface appears to provide superior pressure-redistributing performance compared to sling and flat cross-section foam. This suggests that the CTS could be used as a support surface for many applications, except for individuals with high-level PI risk, without using tilt and recline features.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".