Is Angiosome-Targeted Angioplasty Effective for Limb Salvage and Wound Healing in Diabetic Foot? : A Meta-Analysis
Bibliographic record
Abstract
PURPOSE: Given that the efficacy of employing angiosome-targeted angioplasty in the treatment of diabetic foot remains controversial, this study was conducted to examine its efficacy. METHODS: We performed a systematic literature review and meta-analysis using core databases, extracting the treatment modality of angiosome-targeted angioplasty as the predictor variable, and limb salvage, wound healing, and revision rate as the outcome variables. We used the Newcastle-Ottawa Scale to assess the study quality, along with the Cochrane Risk of Bias Tool. We evaluated publication bias using a funnel plot. RESULTS: The search strategy identified 518 publications. After screening these, we selected four articles for review. The meta-analysis revealed that overall limb salvage and wound healing rates were significantly higher (Odds ratio = 2.209, 3.290, p = 0.001, p<0.001) in patients who received angiosome-targeted angioplasty than in those who received nonangiosome-targeted angioplasty. The revision rate between the angiosome and nonangiosome groups was not significantly different (Odds ratio = 0.747, p = 0.314). CONCLUSION: Although a further randomized controlled trial is required for confirmation, angiosome-targeted angioplasty in diabetic foot was more effective than nonangiosome-targeted angioplasty with respect to wound healing and limb salvage.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.031 |
| Bibliometrics | 0.005 | 0.005 |
| 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) | 0.004 | 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".