Implant surface modifications as a prevention method for periprosthetic joint infection caused by <i>Staphylococcus aureus</i>: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract. Background: Periprosthetic joint infection is the most common infection due to joint replacement. It has been reported that, over a 5-year time span, 3.7 % of cases occurred annually. This statistic has increased to 6.86 % over 16 years. Thus, an effective method is required to reduce these complications. Several strategies such as coating methods with various materials, such as antibiotics, silver, and iodine, have been reported. However, the best preventive strategy is still undetermined. Therefore, this systematic review aims to evaluate the outcome of coating methods on joint arthroplasty as a treatment or preventive management for infection complications. Methods: Eligible articles were systematically searched from multiple electronic databases (PubMed, Cochrane library, and ScienceDirect) up to 2 June 2022. Based on the criterion inclusion, eight articles were selected for this study. The Newcastle–Ottawa scale (NOS) was used to assess the quality of the study, and the meta-analysis test was conducted with Review Manager 5.4. Results: The quality of the articles in this study is in the range of moderate to good. It was found that the application of modified antibiotic coatings significantly reduced the occurrence of periprosthetic joint infection (PJI) (p 0.03), and silver coating could not significantly (p 0.47) prevent the occurrence of PJI. However, according to the whole aspect of coating modification, the use of antibiotics, silver, and iodine can minimize the occurrence of PJI (p <0.0001). Conclusion: Coating methods using antibiotics are an effective method that could significantly prevent the occurrence of PJI. On the other hand, coating with non-antibiotic materials such as silver could not significantly prevent the incidence of PJI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".