Long term monitoring of a pressure ulcer risk patient using thermal images
Bibliographic record
Abstract
Patients in an immobile state are susceptible to pressure ulcers, which are localized injuries to the skin and/or underlying tissues due to prolonged pressure. This paper builds upon a body of work examining in-hospital older adult patients at-risk of developing pedal pressure ulcers by examining thermal images of one patient, who was reporting pain in her right foot, over 112 days of a hospital stay. Thermal images of the patient's left and right heels and malleoli were subjected to image processing to remove noise and enhance contrast, region selection and feature extraction to observe changes in temperature over time. Mean intensity within each ROI was extracted, and the difference in temperature between the left and right heels was calculated over time. The resulting temperature pattern was consistent with the physical phenomenon related to ulcer development, intervention and recovery; the right heel was similar in temperature when starting the study and at the end of the study, but was drastically warmer when experiencing erythema and drastically colder when experiencing ischaemia. These results suggest that consistent thermal imaging, in conjunction with image processing may be able to detect the formation of pressure ulcers faster than can be visually observed. Early detection of pressure ulcers is critical in the prevention of pressure ulcers, and is of great importance to any hospital or nursing home.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 teacher head, 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".