Using Near-Infrared Spectroscopy and Education to Support Older Adults with Diabetic Foot Ulcers to Age-in-Place: A Case Series
Bibliographic record
Abstract
ABSTRACT: The objective of this article is to demonstrate the added value of foot care provided by an RN with foot care training to older adults in their home by focusing on four older adults with diabetic foot ulcers. The RN used a mobile multispectral near-infrared spectroscopy device to enhance the assessment of the diabetic foot ulcers. The Mobile Seniors Wellness Network methodically engaged with English-speaking adults 55 years and older living within a 90-minute radius of the city's community health center. Older adults were referred to the research project through various sources. The participation group included 366 participants with varying holistic healthcare concerns and financial stressors that impacted their ability to age well in place. Over the course of visits by the RN and registered social worker, positive outcomes were facilitated through the collaboration of the participant and the multidisciplinary team, thus enhancing the individual's confidence to remain at home longer. In a time of ongoing provincial health crisis, it may be cost-effective to provide in-home support to those who want to age well in their communities by deploying a Mobile Seniors Wellness Network system throughout the province and enhancing the RN's assessment of feet with a portable and innovative technology tool.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".