Risk factors for the recurrence of diabetic foot ulcers among diabetic patients: a meta‐analysis
Bibliographic record
Abstract
This study aimed to systematically review and identify the risk factors for the recurrence of diabetic foot ulcers (DFUs) among diabetic patients. PUBMED, EMBASE, Web of Science, Cochrane Library, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), WanFang, and VIP databases were electronically searched to identify eligible studies updated to January 2019 to collect case-control studies or cohort studies on the risk factors for the recurrence of DFUs. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias of included studies using the Newcastle-Ottawa Scale. A meta-analysis was performed using RevMan 5.3. Nine retrospective cohort studies were included, in which 1426 patients were enrolled, 542 in the DFU recurrence group and 884 in the non-recurrent DFU group. Risk factors for the recurrence of DFUs included male gender (odds ratio [OR] = 1.38, 95% confidence interval [CI], 1.07-1.78, P < .05), smoking (OR = 1.66, 95% CI, 1.26-2.20, P = .0004), duration of diabetes (WMD = 4.43, 95% CI, 1.96‐6.90, P = .0004), duration of past DFUs (OR = 1.02, 95% CI, 1.00-1.03, P = .006), plantar ulcers (OR = 5.31, 95% CI, 4.93-5.72, P <.00001), peripheral artery disease (OR = 1.65, 95% CI, 1.20-2.28, P = .002), and diabetic peripheral neuropathy (OR = 2.15, 95% CI, 1.40-3.30, P = .0005). No significant differences were found in age, body mass index, total cholesterol, diabetic nephropathy, diabetic retinopathy, or hypertension. Health care staff should pay attention to the identified risk factors for the recurrence of DFUs. Because of the limited quality and quantity of the included studies, rigorous studies with adequate sample sizes are needed to verify the conclusion.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.044 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".