Global Differences in the Management of <i>Staphylococcus aureus</i> Bacteremia: No International Standard of Care
Bibliographic record
Abstract
BACKGROUND: Despite being the leading cause of mortality from bloodstream infections worldwide, little is known about regional variation in treatment practices for Staphylococcus aureus bacteremia (SAB). The aim of this study was to identify global variation in management, diagnostics, and definitions of SAB. METHODS: During a 20-day period in 2022, physicians throughout the world were surveyed on SAB treatment practices. The survey was distributed through listservs, e-mails, and social media. RESULTS: In total, 2031 physicians from 71 different countries on 6 continents (North America [701, 35%], Europe [573, 28%], Asia [409, 20%], Oceania [182, 9%], South America [124, 6%], and Africa [42, 2%]) completed the survey. Management-based responses differed significantly by continent for preferred treatment of methicillin-susceptible S. aureus (MSSA) and methicillin-resistant S. aureus (MRSA) bacteremia, use of adjunctive rifampin for prosthetic material infection, and use of oral antibiotics (P < .01 for all comparisons). The 18F-FDG PET/CT scans were most commonly used in Europe (94%) and least frequently used in Africa (13%) and North America (51%; P < .01). Although most respondents defined persistent SAB as 3-4 days of positive blood cultures, responses ranged from 2 days in 31% of European respondents to 7 days in 38% of Asian respondents (P < .01). CONCLUSIONS: Large practice variations for SAB exist throughout the world, reflecting the paucity of high-quality data and the absence of an international standard of care for the management of SAB.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".