Mobile Health–Based Thermometer for Monitoring Wound Healing After Endovascular Therapy in Patients With Chronic Foot Ulcer: Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Foot temperature may increase after endovascular therapy, but the relationship between foot temperature and wound healing is unclear. OBJECTIVE: This study was performed to evaluate the feasibility of a mobile health (mHealth)-based thermometer for foot temperature monitoring in patients with chronic foot ulcer before and after endovascular therapy and to determine the association between temperature change and wound healing time. METHODS: This was a prospective cohort study. Patients who had a chronic foot ulcer (>3 months) and underwent endovascular therapy between July 2019 and December 2019 were included. The participants received standard medical care and endovascular therapy for revascularization. The mHealth-based thermometer, composed of 4 temperature-sensing chips, was put on the foot before and after endovascular therapy. Data from the chips were transferred to an associated mobile phone app via Bluetooth. Wound healing time was estimated using the Kaplan-Meier method, and the associations between baseline characteristics and clinical outcomes were evaluated using a Cox proportional hazard model. RESULTS: A total of 163 patients with chronic foot ulcer who underwent endovascular therapy were enrolled and followed up until wound healing was complete or for 180 days. The mean foot temperature before endovascular therapy was 30.6 (SD 2.8 °C). Foot temperature increased significantly (mean 32.1 °C, SD 2.8 °C; P=.01) after the procedure. Wound healing time was significantly different in the Kaplan-Meier curves of the patient group with temperature changes ≥2 °C and the group with temperature changes ≤2 °C (log-rank P<.001). A foot temperature increase ≥2 °C after endovascular therapy was associated with increased wound healing in univariate analysis (hazard ratio [HR] 1.78, 95% CI 1.24-2.76, P=.02), and the association remained significant in multivariate analysis (HR 1.69, 95% CI 1.21-2.67, P=.03). CONCLUSIONS: The mHealth-based thermometer was feasible and useful for foot temperature monitoring, which may provide health care professionals with a new endpoint for endovascular therapy. Foot temperature increases ≥2 °C after endovascular therapy were associated with faster wound healing in patients with chronic foot ulcer. Further studies are needed, however, to confirm these findings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".