Impact of diabetes on patients undergoing total ankle arthroplasty: a meta-analysis and systematic review
Bibliographic record
Abstract
Total ankle arthroplasty (TAA) is an effective treatment for end-stage ankle arthritis. However, the procedure is not without risks due to various factors, one of which is diabetes mellitus (DM). Currently, it remains uncertain whether diabetes is a risk factor for increased adverse outcomes and complications following total ankle arthroplasty. Therefore, this study aims to investigate the impact of diabetes on patients undergoing TAA. A systematic search was conducted for relevant studies published before December 2023 in PubMed, Embase, Cochrane Library, and Web of Science. The study assessed demographic data, postoperative complications, and functional outcomes of diabetic and non-diabetic patients following primary TAA. The Newcastle-Ottawa Scale (NOS) was used to evaluate study quality, and meta-analysis was performed using Stata 15.1, with forest plots generated for each variable. This meta-analysis included 14 studies involving 20,557 patients (3,847 with diabetes and 16,710 without). Compared to non-diabetic patients, those with diabetes had higher revision rates, postoperative infection rates, and 30-day readmission rates, longer hospital stays, and significantly different improvements in the SF-36 Physical Component Summary (PCS) score. Diabetic patients undergoing TAA are more likely to require revision surgery, face a higher risk of surgical site infections or periprosthetic joint infections, and experience increased hospital stay and 30-day readmission rates. These findings are crucial for guiding perioperative management of diabetic patients undergoing TAA and for explaining the associated surgical risks to patients.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".