Procalcitonin and Diabetic Foot Ulcer Infections: A Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Background Procalcitonin (PCT) is an effective inflammatory marker for diagnosing infection. We assessed the clinical utility of procalcitonin in diagnosing diabetic foot infections. Method This meta‐analysis adhered to the PRISMA guidelines. We searched PubMed, Web of Science, Embase and the Cochrane Library for studies on PCT for the diagnosis of diabetic foot published before 1 July 2024. The primary outcome was the standardised mean difference (SMD) in PCT levels between IDFU and non‐IDFU groups, with corresponding 95% confidence intervals (CI). The included studies were cross‐sectional and cohort studies, so the quality of the literature was assessed using the Newcastle–Ottawa Scale (NOS) evaluation criteria. This study's statistical analyses were conducted solely with STATA 15.0 software. Result Ten studies comprising 928 patients were ultimately included. There were six cross‐sectional studies and four cohort studies. In total, 532 patients were assigned to the IDFU group and 396 to the non‐infected diabetic foot ulcers (NIDFU) group. The relationship between PCT and DFU was evaluated in ten studies, with significant heterogeneity among the included studies (x2 = 54.10, p = 0.00001; I2 = 83.6%). Therefore, a random effects model was used with a pooled standardised mean difference of 0.79 (95% confidence interval [CI]: 0.43–1.14). The Egger experiment results (t = 0.43, p = 0.680) indicated that there was no publication bias. Analysis of sensitivity revealed that the results were reliable. Subgroup analyses identified the area as a significant source of heterogeneity. The random‐effects model's meta‐regression results revealed that BMI (p = 0.026) and HbA1c (p = 0.016) had a significant impact on the heterogeneity of the association between IDFU and PCT levels. Conclusion Our study showed a significant correlation between serum PCT levels and IDFU. Identification and treatment of IDFUs as soon as possible can help reduce amputation and mortality rates. This systematic review and meta‐analysis evaluated the association between serum procalcitonin levels and diabetic foot infections. Ten studies were included, and a random‐effects model showed significantly higher procalcitonin levels in infected patients, supporting its role as a potential diagnostic biomarker for early infection detection in diabetic foot ulcers.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.059 |
| 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) | 0.004 | 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".