Tertiary care centre adherence to unified guidelines for management of periprosthetic joint infections: a gap analysis
Bibliographic record
Abstract
Background: The success rate of surgical treatment for periprosthetic joint infection (PJI) remains inconsistent in the literature. Variability in PJI clinical guidelines and surgeon adherence to guidelines could affect treatment success. The objectives of this study were to appraise current recommendations for PJI management and develop a unified clinical standard of care, to perform a gap analysis of PJI cases in a tertiary institution to determine the rate of guideline adherence, and to determine if adherence to unified PJI guidelines affected 2-year treatment outcomes. Methods: We appraised the PJI guidelines from 3 academic medical societies, and consistent statements were aggregated. We retrospectively reviewed all PJI cases in a tertiary care institution. We defined PJI based on Musculoskeletal Infection Society PJI criteria. Surgeon adherence to preoperative, intraoperative, surgical and medical management guidelines was calculated, and we evaluated the association between guideline adherence and 2-year treatment outcomes. Results: The institutional rate of PJI was 1.13% (38 of 3368). Treatment success was 57.8% at 2 years. Unified guideline adherence percentages varied substantially: 92% of patients had preoperative erythrocyte sedimentation rate and C-reactive protein, 97% had intraoperative tissue cultures, 42% had appropriate preoperative arthrocentesis, and 74% underwent guideline-appropriate surgery. Performing appropriate preoperative arthrocentesis significantly correlated with positive treatment outcomes at 2 years (p = 0.028). Conclusion: Adherence to PJI guidelines varies considerably, indicating that clinicians are either unaware of them or do not recognize their value for PJI treatment. This study shows the need for institution-based PJI treatment pathways that are consistent with published guidelines and the need to monitor adherence.
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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 | 0.014 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".