Study protocol: A systematic review and meta-analysis of risk factors for the first-ever foot ulcers in patients with diabetes.
Bibliographic record
Abstract
Background Diabetic foot ulcers (DFUs) are a serious complication of diabetes mellitus (DM), significantly contributing to mortality and morbidity in this population. Annually, approximately 18.6 million people with DM develop DFUs, with up to 34% experiencing a foot ulcer during their lifetime. DFUs are a leading cause of limb and life-threatening infections, lower limb amputations, and increased hospitalisations. Despite these public health challenges, there is a lack of research focusing on the primary prevention of DFUs, particularly the prevention of first-ever ulceration. Objectives To systematically review and synthesise research evidence and meta-analysis of previous research findings and derive conclusions regarding risk factors for the development of first-ever foot ulcers in patients with diabetes. Methods and analysis Four English and three Chinese databases will be utilised to identify eligible studies reporting risk factors for the first-ever foot ulcers in patients with diabetes. Two independent researchers will review the literature, extract relevant data, and assess the risk of bias of included studies using the Newcastle-Ottawa Scale. Pooled odds ratios (ORs) and standardised mean differences (SMD) with 95% confidence intervals (CIs) for nominal and continuous data will be calculated employing either a fixed-effects or random-effects model based on heterogeneity ( I ² < 50% for fixed-effects and I ² > 50% for random-effects models). All statistical analyses will be conducted using Stata Software Version 16. Systematic review registration PROSPERO (CRD42024508855)
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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 | 0.043 | 0.071 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.021 | 0.022 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.060 | 0.006 |
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".