Is the routine use of local antibiotics in the management of periprosthetic joint infections justified?
Bibliographic record
Abstract
Periprosthetic joint infection (PJI) following total hip and total knee arthroplasty continues to be a leading cause of re-operation and revision arthroplasty. Not only is the treatment of PJI notoriously challenging, but success rates are variable. Regardless of the surgical strategy used, successful management of PJI requires a comprehensive surgical debridement focused at eradicating the underlying biofilm followed by appropriate antimicrobial therapy. Although systemic antimicrobial delivery continues to be a cornerstone in the treatment of PJI, many surgeons have started using local antibiotics to deliver higher concentrations of antibiotics directly into the vulnerable joint and adjacent soft tissues, which often have compromised vascularity. Available evidence on the use of topical powder, bone cement, and calcium sulphate carriers for local delivery of antibiotics during the initial treatment of PJI is limited to studies that are extremely heterogeneous. There is currently no level-1 evidence to support routinely using these products. Further, appropriately powered, prospective studies are needed to quantify the safety and efficacy of antibiotic-located calcium-sulphate carriers to justify their added costs. These products should not encourage surgeons to deviate from best practice guidelines, such as those recommended during the International Consensus Meeting on Musculoskeletal Infections.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".