Improving Surveillance for Surgical Site Infections Following Total Hip and Knee Arthroplasty Using Diagnosis and Procedure Codes in a Provincial Surveillance Network
Bibliographic record
Abstract
OBJECTIVE To evaluate hospital administrative data to identify potential surgical site infections (SSIs) following primary elective total hip or knee arthroplasty. DESIGN Retrospective cohort study. SETTING All acute care facilities in Alberta, Canada. METHODS Diagnosis and procedure codes for 6 months following total hip or knee arthroplasty were used to identify potential SSI cases. Medical charts of patients with potential SSIs were reviewed by an infection control professional at the acute care facility where the patient was identified with a diagnosis or procedure code. For SSI decision, infection control professionals used the National Healthcare Safety Network SSI definition. The performance of traditional surveillance methods and administrative data-triggered medical chart review was assessed. RESULTS Of the 162 patients identified by diagnosis or procedure code, 46 (28%) were confirmed as an SSI by an infection control professional. More SSIs were identified following total hip vs total knee arthroplasty (42% vs16%). Of 46 confirmed SSI cases, 20 (43%) were identified at an acute care facility different than their procedure facility. Administrative data-triggered medical chart review with infection control professional confirmation resulted in a 1.1- to 1.7-fold increase in SSI rate compared with traditional surveillance. SSIs identified by administrative data resulted in sensitivity of 90% and specificity of 99%. CONCLUSION Medical chart review for cases identified through administrative data is an efficient supplemental SSI surveillance strategy. It improves case-finding by increasing SSI identification and making identification consistent across facilities, and in a provincial surveillance network it identifies SSIs presenting at nonprocedure facilities. Infect Control Hosp Epidemiol 2016;37:699-703.
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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 | 0.002 | 0.003 |
| 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".