Inflammatory Markers in Diabetic Foot Infection: A Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: Diabetic foot infection is a serious and painful process for patients with diabetes, and the considerable morbidity associated with the condition warrants attention. Effective inflammatory markers may become important in the detection of diabetic foot infection. OBJECTIVE: The goal of the research was to systematically assess the function of inflammatory markers in the detection of diabetic foot infection. METHODS: Online databases including PubMed, SpringerLink, and Web of Science were searched. The quality of research and data was assessed using the Newcastle-Ottawa Scale. A random-effects model was used to compare changes in inflammatory markers between patients with infected diabetic foot (IDF) and patients with non-infected diabetic foot. RESULTS: Ten studies with 785 participants were included in the systematic review. The study analyzed 3 inflammatory markers: white blood cell (WBC) count, C-reactive protein (CRP) level, and procalcitonin (PCT) level. The meta-analysis indicated that mean WBC count (standardized mean differences [SMD]: 0.51, 95% CI: 0.23, 0.79; P < .0001), mean CRP level (SMD: 1.05, 95% CI: 0.60, 1.50; P < .0001) and mean PCT level (SMD: 0.80, 95% CI: 0.36, 1.24; P < .0001) were higher in patients with IDF. The differences were statistically significant, but the funnel plots indicated the existence of publication bias. CONCLUSIONS: The meta-analysis further confirmed the significant association between inflammatory markers and diabetic foot infection. It also confirmed that WBC count, CRP level, and PCT level can be used as laboratory auxiliary indexes in the detection of diabetic foot infection, providing information for improved diagnosis and prevention.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.055 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".