Use of a Provincial Surveillance System to Characterize Post-Operative Surgical Site Infections Following Primary Hip and Knee Arthroplasty in Alberta, Canada
Bibliographic record
Abstract
Background. Knee and hip replacements are an effective intervention for improving patient quality of life. Rates of these surgeries in North America are growing, coinciding with increasing frequency of obesity and an aging population. A small number of these surgeries will develop post-operative surgical site infections (SSI), causing substantial patient morbidity and healthcare costs. We present a population based descriptive analysis of SSI following primary hip and knee replacements. Methods. The Alberta Health Services Infection Prevention and Control program collects data prospectively on all SSI after primary total hip and knee arthroplasty done in Alberta, Canada. The data used here contained all SSI within 180 days of surgical procedures between 1 March 2012 and 30 September 2014. Primary outcomes were rate of infection and causative pathogens. Secondary outcomes included timing of infection after surgery (i.e. 30 days or less, 31-90 days, and greater than 90 days), and the relationship of methicillin-resistant Staphylococcus aureus (MRSA) colonization to infection. Results. There were 312 SSI cases for review. Overall rate of SSI (per 100 procedures) were 1.69 and 1.20 for hip and knee replacements, respectively. The majority (79%) of infections occurred within the first 30 days following surgery. When stratified by time to infection, the proportion of knee SSIs increased from 47 to 67 percent after 30 days. Causative pathogens were identified in 122 (80%) hip infections and 116 (72.5%) knee infections. Most commonly identified was Staphylococcus aureus (38%), and the type of organism isolated was not related to the timing of infection. Colonization with MRSA was associated with subsequent infection (OR 40). Conclusion. From this study, we have identified several important characteristics of these infections that may be helpful for determining optimal prevention strategies. For example, intensive post-operative follow up within 30 days of primary knee arthroplasty may help prevent a subsequent SSI. Additionally, decolonization techniques may decrease consequent MRSA SSI in colonized patients. Disclosures. All authors: No reported disclosures.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".