Effect of contact with podiatry in a team approach context on diabetic foot ulcer and lower extremity amputation: systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Multidisciplinary team (MDT) approach has been shown to reduce diabetic foot ulcerations (DFUs) and lower extremity amputations (LEAs), but there is heterogeneity between team members and interventions. Podiatrists have been suggested as “gatekeepers” for the prevention and management of DFUs. The purpose of our study is to review the effect of podiatric interventions in MDTs on DFUs and LEAs. We conducted a systematic review of available literature. Data's heterogeneity about DFU outcomes made it impossible for us to include it in a meta‐analysis, but we identified 12 studies fulfilling inclusion criteria that allowed for them to be included for LEA outcomes. With the exception of one study, all reported favourable outcomes for MDTs that include podiatry. We found statistical significance in favour of an MDT approach including podiatrists for our primary outcome (total LEAs (RR: 0.69, 95% CI 0.54–0.89, I2 = 64%, P = 0.002)) and major LEAs (RR: 0.45, 95% CI 0.23–0.90, I2 = 67%, P < 0.02). Our systematic review, with a standard search strategy, is the first to specifically address the relevant role of podiatrists and their interventions in an MDT approach for DFU management. Our observations support the literature that MDTs including podiatrists have a positive effect on patient outcomes but there is insufficient evidence that MDTs with podiatry management can reduce the risk of LEAs. Our study highlights the necessity for intervention descriptions and role definition in team approach in daily practice and in published literature.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".