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
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.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".