How to Prevent Wound Complications After Total Ankle Arthroplasty Through Anterior Approach: A Systematic Review on Current Treatment Options
Bibliographic record
Abstract
Introduction Total ankle arthroplasty (TAA) through anterior approaches is a common treatment for end-stage tibiotalar arthritis. The occurrence of wound healing problems can lead to severe consequences. The aim of this systematic review is to summarize the available methods to minimize postoperative wound complications after TAA through standard anterior approaches. Methods Three databases were searched for original articles concerning methods to reduce anterior wound complications after TAA. Eligible articles were examined to extract studies’ characteristics, population data, type of intervention, and related wound complications. Study risk of bias assessment was conducted through the Newcastle-Ottawa Scale. Results Thirteen articles were included for analysis, investigating 8 types of intervention, which were grouped into 3 classes: biological, mechanical, and pharmacological methods. A significant decrease in wound complications was reported for negative pressure wound therapy (3% vs 24%, P = .014), soft tissue expansion strips (2% vs 12%, P = .04), and tranexamic acid (TXA) administration (9% vs 22%, P = .002). Conclusion Despite the limitations of the included studies, this review showed encouraging results for TXA administration. Good results were found for mechanical methods, despite each intervention being supported by only 1 comparative study. Careful selection of patients is recommended to identify potential benefits or contraindications to such interventions. Further prospective randomized studies would be helpful to confirm these results. Levels of Evidence : 3
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 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) | 0.006 | 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".