Do prior intra-articular injections impact on the risk of periprosthetic joint infection in patients undergoing total hip arthroplasty? A meta-analysis of the current evidences with a focus on the timing of injection before surgery
Bibliographic record
Abstract
Purpose: Intra-articular injection is a well-established and increasingly used treatment for the patient with mild-to-moderate hip osteoarthritis. The objectives of this literature review and meta-analysis are to evaluate the effect of prior intra-articular injections on the risk of periprosthetic joint infection (PJI) in patients undergoing total hip arthroplasty (THA) and to try to identify which is the minimum waiting time between hip injection and replacement in order to reduce the risk of infection. Methods: The database of PubMed, Embase, Google Scholar and Cochrane Library was systematically and independently searched, according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To assess the potential risk of bias and the applicability of the evidence found in the primary studies to the review, the Newcastle-Ottawa scale (NOS) was used. The statistical analysis was performed by using the software 'R' version 4.2.2. Results: The pooling of data revealed an increased risk of PJI in the injection group that was statistically significative (P = 0.0427). In the attempt to identify a 'safe time interval' between the injection and the elective surgery, we conducted a further subgroup analysis: in the subgroup 0-3 months, we noted an increased risk of PJI after injection. Conclusions: Intra-articular injection is a procedure that may increase the risk of developing periprosthetic infection. This risk is higher if the injection is performed less than 3 months before hip replacement.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.060 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 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".