Evaluating patient turn effectiveness using turn-assist technologies
Bibliographic record
Abstract
Pressure ulcers are commonly developed in bedridden patients due to prolonged pressure on bony prominences. Turn-assist support surfaces have been developed to help reposition patients to redistribute interface pressure. The aim of this study was to determine if turn-assist technologies confer benefits to patients relative to manual turning, and to determine if different turn-assist functionalities influence patient outcomes differently. Interface pressure (contact area, average and peak pressure) and patient turn quality metrics (turn angle and repeatability) were recorded during manual and facilitated turns on two different turn-assist hospital beds at initial patient position, turn-assist (maximal mattress inflation) and final patient position. Manual turns produced the most repeatable turn angles, and closest to the recommended 30° compared to both turn-assist surfaces. Interface pressure differences between surfaces were most prominent in the pelvis region across all three time points. Overall, turn-assist surfaces produced interface pressure outcomes similar to manual turning, but manual turning produced more repeatable and optimal patient turn angles. Different turn-assist surfaces achieved different patient turn angles, so functionalities should be examined before device implementation.
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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 | 0.002 | 0.010 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".