Monitoring the relief of pressure points for pressure ulcer prevention: A subject dependent approach
Bibliographic record
Abstract
Pressure ulcers are of great cost to both the patient and the healthcare system. Devices have been developed with the goal of pressure ulcer prevention, but many available technically complex devices have been shown to be no more effective than low pressure overlays or mattresses. This paper proposes a subject dependent algorithm capable of automatically detecting when and where pressure points have been relieved from underneath a supine subject, without any user inputs or assumptions. Pressure sensitive mats, associated software, a laptop and a video camera were used to measure and collect pressure signals generated by a supine subject performing 3 movements: the subject rolling to one side of the body, then to the other side, and the subject attempting to roll without lifting any pressure points off the mattress. The data was zeroed, baseline values were found, differences in sensor score from baseline were calculated, and instances during which a valley on one side coincided with a peak on the other, were recorded. Examination of these results indicated that the algorithm was capable of determining when and where pressure points underneath the sacrum and foot regions were lifted off the bed, but not capable of determining if a scapula pressure point was relieved. These results suggest that the proposed algorithm is effective for some, but not all regions of the body. Future work will therefore focus on detection of all pressure points, and the adjustment of the algorithm for subject independence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".