The Efficacy of Defensive Antibacterial Coating (DAC™) Periprosthetic Joint Infection Prevention in the Hip: A Systematic Review
Bibliographic record
Abstract
Background: Periprosthetic joint infections (PJIs) are a significant issue in joint replacement surgery patients, affecting results and mortality. Recent research focuses on developing hydrogels (HG) and antimicrobial coatings to reduce pressure injuries, with DAC™ HG showing lower infection risk in hip revision surgery. However, the effectiveness of DAC™ hydrogel in PIJs is still unknown. Here, we attempt to update the literature in this field, pointing out methodological flaws and providing guidance for further research. Methods: We conducted a systematic literature review using the PRISMA guidelines. Quality assessment was performed with the Newcastle–Ottawa Scale (NOS) and the Coleman Methodology Score (CMS). Results: Among 27 records from the initial search, 3 studies resulted eligible for final evaluation. It was observed that following the three surgical procedures performed in combination with DAC™ loaded with specific antibiotics, the quality of life of the treated patients had improved. No side effects associated with DAC™ treatment were in fact observed. Conclusions: The amount and quality of scientific evidence are yet insufficient to either encourage or dissuade the use of such hydrogels in hip prosthesis, despite some intriguing first results. These challenges will be better addressed by randomized controlled trials or longitudinal prospective investigations.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".