Footwear fit as a causal factor in diabetes‐related foot ulceration: A systematic review
Bibliographic record
Abstract
AIMS: Incorrectly fitting footwear (IFF) poses a risk of trauma to at-risk feet with diabetes. The aim of this systematic review was to summarise and assess the evidence that IFF is a statistically significant cause of ulceration. METHODS: We searched PubMed, Scopus, Web of Science and Google Scholar for English-language peer-reviewed studies reporting the number or percentage of people with diabetes-related foot ulceration (DFU) attributed to wearing IFF and included a physical examination of the footwear worn. Two independent reviewers assessed the risk of bias using the Newcastle-Ottawa scale. RESULTS: 4318 results were retrieved excluding duplicates with 45 studies shortlisted. Ten studies met the inclusion criteria with most rated as fair (n = 6) or good (n = 3). There is some evidence that DFU is significantly associated with IFF, but this is limited: only 3 of 10 included studies found a statistically significant percentage of those with DFU were wearing IFF or inappropriate footwear which included fastening, material, type or fit (15.0%-93.3%). Risk of bias in these three studies ranged from 'fair' to 'poor'. IFF definitions were often unreported or heterogeneous. Only one study reported IFF-related ulcer sites: 70% were at plantar hallux/toes and 10% at plantar metatarsal heads. CONCLUSIONS: There is some evidence that IFF is a cause of DFU, but further research is needed, which defines IFF, and methodically records footwear assessment, ulcer location and physical activity. Researchers need to uncover why IFF is worn and if this is due to economic factors, a need for footwear education or other reasons.
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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 | 0.008 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".