Bibliographic record
Abstract
Periprosthetic joint infection (PJI) is a devastating and costly complication following total joint arthroplasty, placing its prevention and effective treatment at the top of the list of priorities for both orthopaedic surgeons and the infectious disease community1–5. PJI impacts patients physically, socially, and emotionally as a result of elevated hospital readmission rates, costly repeat surgeries, prolonged hospital stays, increased utilization of outpatient services, and protracted antibiotic therapy, as well as the increased risk of death6. Increased mortality following PJI is well documented; however, the studies demonstrating these findings have been small and somewhat imprecise and have often exhibited confounding. In a study featured in this issue of JBJS, Mundi and colleagues investigated the 10-year mortality risk of PJI following total hip arthroplasty, after controlling for relevant confounders7. A total of 175,432 patients with a primary total hip replacement were identified from a large Canadian health information database. Of these, 868 patients (0.49%) underwent surgery for a PJI within 1 year after total hip arthroplasty. After matching patients by age (±1 year), sex (male or female), Class-III obesity, year of surgery, and the logit of a propensity score for PJI occurrence, patients with an infection within the first year had a significantly higher 10-year mortality rate (11.4%). This approximates a fivefold increased risk of mortality. In a sensitivity analysis, patients were also matched by primary surgeon, and the association between PJI and mortality remained. The study by Mundi et al. has several important strengths, including sample size, appropriate controls, and a relatively precise confidence interval for the overall estimates of mortality. Even in the most optimistic scenario, their data suggest that PJI results in a threefold increase in mortality, but this increase could be as high as ninefold. These increased rates mirror the mortality rates associated with some common adult cancers. Unlike patients with malignancy or cardiovascular disease, the vast majority of patients undergoing a revision joint replacement for PJI may not be aware of the associated elevated mortality risk. Although this study is not without limitations, it helps to shed light on the devastating complication of PJI following joint replacement surgery—and serves as a call to action to researchers to design, execute, and disseminate high-quality studies on PJI prevention and treatment.
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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 | 0.041 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.022 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.036 | 0.038 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".