Periprosthetic Infection following Primary Hip and Knee Arthroplasty: The Impact of Limiting the Postoperative Surveillance Period
Bibliographic record
Abstract
BACKGROUND Hip and knee arthroplasty infections are associated with considerable healthcare costs. The merits of reducing the postoperative surveillance period from 1 year to 90 days have been debated. OBJECTIVES To report the first pan-Canadian hip and knee periprosthetic joint infection (PJI) rates and to describe the implications of a shorter (90-day) postoperative surveillance period. METHODS Prospective surveillance for infection following hip and knee arthroplasty was conducted by hospitals participating in the Canadian Nosocomial Infection Surveillance Program (CNISP) using standard surveillance definitions. RESULTS Overall hip and knee PJI rates were 1.64 and 1.52 per 100 procedures, respectively. Deep incisional and organ-space hip and knee PJI rates were 0.96 and 0.71, respectively. In total, 93% of hip PJIs and 92% of knee PJIs were identified within 90 days, with a median time to detection of 21 days. However, 11%-16% of deep incisional and organ-space infections were not detected within 90 days. This rate was reduced to 3%-4% at 180 days post procedure. Anaerobic and polymicrobial infections had the shortest median time from procedure to detection (17 and 18 days, respectively) compared with infections due to other microorganisms, including Staphylococcus aureus. CONCLUSIONS PJI rates were similar to those reported elsewhere, although differences in national surveillance systems limit direct comparisons. Our results suggest that a postoperative surveillance period of 90 days will detect the majority of PJIs; however, up to 16% of deep incisional and organ-space infections may be missed. Extending the surveillance period to 180 days could allow for a better estimate of disease burden. Infect Control Hosp Epidemiol 2017;38:147-153.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".