Effectiveness of Multidisciplinary Approaches in Managing Diabetic Foot Ulcers. A Systematic Review
Bibliographic record
Abstract
Background: Diabetic foot ulcers (DFUs) are a significant complication of diabetes, associated with high morbidity, reduced quality of life, and increased healthcare costs. Effective management of DFUs often requires an integrated approach due to their multifactorial nature. This systematic review evaluates the effectiveness of multidisciplinary approaches in the management of DFUs, highlighting clinical outcomes, prevention of complications, and quality of life improvements.Methods: A comprehensive literature search was conducted in databases including PubMed, Scopus, Cochrane Library, and Web of Science, covering studies published from January 2000 to January 2025. Inclusion criteria focused on randomized controlled trials (RCTs), cohort studies, and observational studies investigating multidisciplinary team (MDT) approaches in managing DFUs. Data extraction was performed independently by two reviewers, with a focus on wound healing rates, amputation rates, infection control, recurrence, and patient satisfaction. Risk of bias was assessed using Cochrane tools and the Newcastle-Ottawa Scale.Results: A total of ten studies were included. Multidisciplinary approaches demonstrated superior outcomes compared to standard care in several domains. Wound healing rates were significantly improved, with a pooled mean reduction in healing time of 25% (95% CI: 18-32%, p < 0.001). Amputation rates were reduced by 35% (RR: 0.65, 95% CI: 0.50-0.83), and infection control was more effective due to integrated antimicrobial management. Recurrence rates of DFUs decreased by 30%, and patient-reported outcomes indicated higher satisfaction and quality of life scores. Key components of successful MDTs included endocrinologists, podiatrists, vascular surgeons, wound care specialists, and patient education programs.Conclusions: Multidisciplinary approaches significantly enhance the management of DFUs by improving clinical outcomes and reducing complications. This review underscores the importance of integrated care models in reducing the burden of DFUs on patients and healthcare systems. Further research is needed to standardize MDT protocols and evaluate cost-effectiveness across diverse healthcare settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.009 | 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) | 0.003 | 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".