Antibiotic Cement in Arthroplasty: A Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Introduction Periprosthetic joint infection (PJI) following arthroplasty surgery is a devastating complication. Antibiotic cement has been proposed as a way to reduce PJI rates. The aim of this systematic review and meta-analysis was to review all of the available randomized controlled trial (RCT) evidence on the use of antibiotic cement in arthroplasty. Methods PubMed, MEDLINE, and Embase were searched. All records were screened in triplicate. Eligible RCTs were included. Data regarding study characteristics, patient demographics, and rates of superficial and deep infection were collected. The risk of bias was assessed using the Cochrane Risk of Bias Assessment Tool 2.0. Results Five RCTs were included (n = 4,397). Four studies compared antibiotic cement to plain cement while one study compared high-dose dual-antibiotic (HDDA) cement to low-dose single-antibiotic (LDSA) cement. The mean age of included patients was 76.4 years (range: 68-83). There was no significant difference in superficial infection rates between antibiotic and plain cement (odds ratio (OR): 1.33, 95% Confidence Interval (CI): 0.77-2.30, p = 0.3). There was a large but non-significant reduction in deep infection rates for antibiotic cement (OR: 0.20, 95%CI: 0.03-1.32, p = 0.09). There was a significantly lower rate of infection with HDDA as compared to LDSA (OR: 0.31, 95% CI: 0.09-0.88, p = 0.041). Conclusion The available evidence from RCTs reveals a potential benefit for antibiotic cement in arthroplasty surgery, though this difference is non-significant and highly imprecise. Furthermore, HDDA cement was significantly more effective than LDSA cement. There is a need for large, pragmatic trials on this topic.
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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 | 0.030 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.059 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".