Efficacy of plantar foot temperature monitoring in preventing ulcers in individuals with diabetes: An umbrella review protocol
Bibliographic record
Abstract
Diabetes mellitus often leads to foot ulcers, which can result in amputations and significantly affect quality of life. Monitoring plantar foot temperature has emerged as a promising intervention for preventing these ulcers. This study aims to examine the potential benefits of plantar foot temperature monitoring in preventing ulcers in individuals with diabetes mellitus. The umbrella review protocol will follow the guidelines set forth by the Joanna Briggs Institute. Participants aged 18 years and older with a diagnosis of type 1 or type 2 diabetes mellitus, and without any active ulcers at the commencement of the study, will be included. We will consider studies that monitor plantar foot temperature using thermometry and thermography. Two independent reviewers will carry out the study location selection and data extraction, employing a modified and validated JBI extraction tool. The methodological quality of the studies included will be evaluated using both the JBI Critical Appraisal Checklist for Systematic Reviews and Research Syntheses, as well as the AMSTAR-2 tool. This systematic review is registered under PROSPERO number CRD42024509838. Data on the effectiveness of interventions for monitoring plantar foot temperature to prevent ulcers in individuals with diabetes will be collected and summarized. A citation matrix will analyze the overlap of primary studies, and meta-analysis will be performed if feasible. The certainty of evidence will be assessed using the GRADE system. This protocol ensures rigorous execution by researchers and may aid in implementing evidence-based nursing interventions for ulcer prevention in diabetes.
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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 | 0.085 | 0.093 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.073 | 0.011 |
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".