Minimally Invasive Metatarsal Osteotomies (MIMOs) for the Treatment of Plantar Diabetic Forefoot Ulcers (PDFUs): A Systematic Review and Meta-Analysis with Meta-Regressions
Bibliographic record
Abstract
Plantar Diabetic Foot Ulcers (PDFUs) are frequent injuries affecting and heavily limiting the quality of life in diabetic patients. PDFUs can be treated both conservatively (with a high recurrence rate) or surgically (with a high rate of complication). Recently, minimally invasive surgery (MIS), performed by small incisions, has been increasingly applied on diabetic feet due to their encouraging outcomes and low complication rate. This systematic review with meta-analysis and meta-regression aims to evaluate for the first time the effectiveness of minimally invasive metatarsal osteotomies (MIMOs) in treating PDFUs and reducing their recurrence rate. A literature search of PubMed/MEDLINE, ISI/Web of Science and Scopus databases was carried out with the keywords “(metatarsal osteotom*) OR (metatarsal AND osteotom*) AND diabet* AND (feet OR foot OR forefoot) AND ulcer”, covering the period from 1980 until June 2021 following PRISMA guidelines. The JBI critical appraisal tool was used for Quality Assessment. Healing rate/time, infection rate, recurrence rate, non-union rate and complication rate were evaluated. When possible, these values were pooled and expressed in effect size (ES), and their 95% confidence interval (CI) was computed. Meta-regression analysis (both uni- and multivariate) was conducted. Eight studies were included in the review, including 189 patients. The healing rate of these studies ranged between 55.1 and 100%, infection rate from 3.3 to 31.8%, recurrence rate from 0.0% and 13.6% and non-union rate from 4.5–30.0%. Overall complication rate was reported in three studies and ranged from 44.9 to 68.2%. Meta-analysis of the various rates revealed an overall healing rate of 91.9% (range from 74.9 to 97.8%), infection rate of 10.9% (4.2–25.2%), recurrence rate 7.2% (3.6–14.2%), non-union rate 16.9% (10.2–26.7%) and finally, the complication rate was computed at 53.2%. Satisfactory short- to medium-term clinical and radiographic results were reported by the studies included in this review, supporting that MIMOs represent an effective surgical approach to treat PDFUs. However, poor quality in the methodology of some studies and the lack of long-term data were reported. Therefore, randomized controlled trials, prospective studies and long-term follow-up studies are needed.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".