Implementation of evidence-based foot screening in people with diabetes: A scoping review
Bibliographic record
Abstract
BACKGROUND: Recommendations to prevent diabetes ulceration and amputation include an annual foot check, primarily screening for sensation and circulation. Using these simple, evidence-based components is vital to identifying complications early, assessing risk, and managing care to prevent or delay amputations. However, routine implementation of these assessments is suboptimal and approaches to their integration remain poorly understood. AIM: We aimed to identify and synthesize information on the factors affecting implementation of simple evidence-based diabetes foot screening. METHODS: We reviewed published and grey literature using a blinded two-stage process by two independent reviewers. Included studies were primary research that implemented or improved foot screening for adults with type 1 or 2 diabetes, assessing at least one of the following: 10-g monofilament sensitivity, pedal pulse palpation, or history of ulceration or amputation. A thematic synthesis approach was used. RESULTS: We screened 5133 titles and abstracts, reviewed 102 full-text articles, and included 26 studies in the final analysis. We identified four key themes: (1) Existing diabetes screening (i.e. retinal screening) or treatment interventions (i.e. medication collection) provide opportunities for synergistic integration; (2) Annual event-based foot screening (e.g. on World Diabetes Day) in lower resource settings provides community-focused preventative care; (3) Further opportunities to increase access to foot screening include self-administered screening and screening in complex residential settings; (4) Healthcare provider champions are essential for local foot screening implementation in primary and secondary care. CONCLUSION: Further research should evaluate the issues identified in these four themes, in different contexts, and with support of implementation frameworks.
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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 | 0.047 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".