Measuring Repositioning in Home Care for Pressure Injury Prevention and Management
Bibliographic record
Abstract
Despite the widespread agreement on the need for the regular repositioning of at-risk individuals for pressure injury prevention and management, adherence to repositioning schedules remains poor in the clinical environment. The situation in the home environment is likely even worse. Our team has developed a non-contact system that can determine an individual's position in bed (left-side lying, supine, or right-side lying) using data from a set of inexpensive load cells placed under the bed. This system was able to detect whether healthy participants were left-side lying, supine, or right-side lying with 94.2% accuracy in the lab environment. The objective of the present work was to deploy and test our system in the home environment for use with individuals who were sleeping in their own beds. Our system was able to detect the position of our nine participants with an F1 score of 0.982. Future work will include improving generalizability by training our classifier on more participants as well as using this system to evaluate adherence to two-hour repositioning schedules for pressure injury prevention or management. We plan to deploy this technology as part of a prompting system to alert a caregiver when a patient requires repositioning.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".