Periprosthetic joint infections at a teaching hospital in 1990–2007
Bibliographic record
Abstract
BACKGROUND: Periprosthetic joint infections (PJIs) are major complications associated with high costs and substantial morbidity. We sought to evaluate hip and knee arthroplasty infection rates at our hospital, compare them in periods before and after implementation of measures to reduce PJIs (1990-2002 and 2003-2007) and identify associated risk factors. METHODS: We retrospectively reviewed records of patients who received primary hip or knee total joint prostheses at our centre between Jan. 1, 1990, and Dec. 31, 2007, and were readmitted for the treatment of infection related to their surgery. We also reviewed data from a prospective surveillance protocol of total hip (THA) and knee arthroplasty (TKA) infections that started in November 2005. We ascertained the annual rates of deep, superficial and hematogenous infections. RESULTS: During the periods studied, 2403 THAs and 1220 TKAs were performed. For THA, the average rates of deep, superficial and hematogenous infections were 2.0%, 0.8% and 0.3%, respectively. For TKA, the rates were 1.6%, 0.7% and 0.2%, respectively. Of 106 infected joints, 84 (79.2%) presented risk factors for infection. Efforts to reduce the infection rate at our institution began in 2003. We achieved a 44% decrease in the deep infection rate for THA (2.5% v. 1.4%; p = 0.06) and a 45% decrease for TKA (2.0% v. 1.1%, p = 0.20) between the periods studied. CONCLUSION: Knowing the actual infection rate associated with different procedures in specific settings is essential to identify unexpected problems and seek solutions to improve patient care. Although we do not know what specific improvements were successful, we were able to decrease our infection rates to levels comparable to those reported by similar care centres.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".