Staphylococcus aureus bloodstream infection: Secular changes associated with the implementation of a de novo clinical infectious diseases service in a Canadian population
Bibliographic record
Abstract
OBJECTIVE: To investigate the epidemiology of Staphylococcus aureus bloodstream infections (BSI) in a mixed rural to small city population and examine secular changes associated with the implementation of a regional clinical infectious diseases program. METHODS: Population-based surveillance for incident S. aureus BSI was conducted in the western interior of British Columbia, Canada between April 2010 and March 2020. An infectious diseases service was progressively implemented starting in 2013. RESULTS: 581 incident S. aureus BSI were identified. There was an increasing incidence during the study and the overall age- and gender-adjusted annual rate was 32.9 per 100,000 population. Implementation of the infectious diseases program was associated with an increase in rates of blood culture sampling, documentation of persistent bacteremia, use of transthoracic and transesophageal echocardiography, and a reduction in cases of relapsed BSI. Infectious diseases consultation was independently associated with a reduced risk for death (odds ratio 0.5; 95% CI 0.3-0.9). CONCLUSIONS: Although the implementation of a clinical infectious diseases service was associated with changes in management and improved outcome, S. aureus BSI still causes a major burden of illness.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".