Measurement of Dynamic Forces Applied to Skin During Lateral Patient Transfers Using the ALTA System
Bibliographic record
Abstract
Alternative patient transfer methods and systems are required to improve transfer safety and experience for patients and staff. Current sling and other transfer methods to lift or move patients apply forces to the patient that cause injury, especially for patients with frail skin or other conditions that put them at risk. New solutions must also reduce the risk for staff injuries associated with the physical labour required to transfer patients using slings or other systems. This paper assesses the forces applied to adult mock patients and adult-size manikins using the Able Innovations ALTA transfer system; a new alternative lateral transfer system. A novel sensor is used that was developed to measure normal and shear forces applied to skin. Increased normal forces can cause injury through contusions. This paper shows that with the ALTA transfer system the patient experienced an increased normal force of ~50% over their normal body weight, reduced from an increase of >300% previously reported for sling transfers. Shear forces can cause injury through skin tears with 14.5N shown to cause tissue damage. Again, this paper shows a maximum shear force of 1.4N during transfer that is substantially lower than the 8N shear force previously reported for sling transfers. The work also shows the challenges associated with the measurement of dynamic forces within a complex interaction between a mechatronics system and human subject.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".