The Effect of Surgical Site Infection on Cost and Utilization Following Primary Knee Replacement in Nova Scotia 2005-2014
Bibliographic record
Abstract
IntroductionSurgical site infection (SSI) following joint replacement results in considerable disease burden. Costs include longer wait times for initial joint replacements which, in Nova Scotia (NS), are the longest in the country. Despite the widespread consequences, costs and utilization following knee replacement have not been comprehensively measured in Canada previously.
 Objectives and ApproachThe objective was to measure costs and health care utilization of SSI following knee replacement through linked administrative data sources. The study cohort was constructed using procedure codes from hospital discharge data. Diagnostic variables were examined to determine the occurrence of infection within one year of discharge. A non-infected control group matched on age, sex and comorbidities was also selected. Costs and utilization from inpatient, day surgery, clinic and physician claims data were totaled over two years following discharge. Resource weights multiplied by standard cost was used to measure hospital costs while outpatient costs were government approved payments to physicians.
 ResultsOver the 2005-2014 period, there were 204 infected cases for an overall 1-year rate of 1.8%. Non-infected controls visited a physician or were admitted to the hospital 21 times in the two year period following surgery compared to infected cases who averaged 40 (p-value
 Conclusion/ImplicationsCosts attributable to infection following primary knee replacement are substantial in NS affecting both inpatient and outpatient services. With an increased focus on program evaluation by policy-makers, infection control administrators should include regular monitoring of the direct and indirect costs of SSI on a system-wide basis using linked administrative data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".