A comparison of surgical site infections following total hip replacement and total knee replacement surgeries identified by Infection Prevention and Control and the National Surgical Quality Improvement Program in Alberta, Canada
Bibliographic record
Abstract
OBJECTIVE: To understand how the different data collections methods of the Alberta Health Services Infection Prevention and Control Program (IPC) and the National Surgical Quality Improvement Program (NSQIP) are affecting reported rates of surgical site infections (SSIs) following total hip replacements (THRs) and total knee replacements (TKRs). DESIGN: Retrospective cohort study. SETTING: Four hospitals in Alberta, Canada. PATIENTS: Those with THR or TKR surgeries between September 1, 2015, and March 31, 2018. METHODS: Demographic information, complex SSIs reported by IPC and NSQIP were compared and then IPC and NSQIP data were matched with percent agreement and Cohen's κ calculated. Statistical analysis was performed for age, gender and complex SSIs. A P value <.05 was considered significant. RESULTS: In total, 7,549 IPC and 2,037 NSQIP patients were compared. The complex SSI rate for NSQIP was higher compared to IPC (THR: 1.19 vs 0.68 [P = .147]; TKR: 0.92 vs 0.80 [P = .682]). After matching, 7 SSIs were identified by both IPC and NSQIP; 3 were identified only by IPC, and 12 were identified only by NSQIP (positive agreement, 0.48; negative agreement, 1.0; κ = 0.48). CONCLUSIONS: Different approaches to monitor SSIs may lead to different results and trending patterns. NSQIP reports total SSI rates that are consistently higher than IPC. If systems are compared at any point in time, confidence on the data may be eroded. Stakeholders need to be aware of these variations and education provided to facilitate an understanding of differences and a consistent approach to SSI surveillance monitoring over time.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".