Prescribing Home Digital Thermometry Coupled with Activity Dosing and Optimized Offloading to Prolong Diabetic Foot Remission: A Case Report
Bibliographic record
Abstract
People with a history of diabetic foot ulcers (DFUs) experience diminished health-related quality of life and are at a 40% annual risk of DFU recurrence. Due to a fear of DFU recurrence, people in DFU remission participate less in physical activity and moderate-intensity exercise when compared to people with diabetes who have not had wounds. There is novel evidence to suggest that too little activity during DFU remission contributes to only low magnitudes of repetitive tissue loading creating a higher susceptibility to skin trauma during inadvertent high-activity bouts. Conversely, a hasty return to too much activity could lead to rapid recurrence. There is now high-level evidence from multiple meta-analyses to indicate that home-based foot temperature monitoring, coupled with activity modification and daily inspection of the feet for impending signs of ulceration, could reduce the risk of ulcer recurrence by 50%. There is little evidence, however, to guide the decision-making regarding the appropriate quantity and frequency of physical activity during DFU remission and the acceptability from the patient perspective. This has resulted in limited uptake of this novel intervention in clinical practice. Earlier, we proposed that activity can be dosed for people in foot ulcer remission, just like insulin or medication is dosed. Here, we describe a patient-centered approach to implementing home foot temperature monitoring coupled with daily foot checks and dosage-based return to physical activity in a patient in DFU remission, including his perspective. We believe using such an approach could maximize ulcer-free days in remission, thereby improving quality of life.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| 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".