Comparative effectiveness of β-lactam versus vancomycin empiric therapy in patients with methicillin-susceptible Staphylococcus aureus (MSSA) bacteremia
Post-publication record
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Bibliographic record
Abstract
BACKGROUND: Vancomycin may be inferior to β-lactams for the empiric treatment of methicillin-susceptible Staphylococcus aureus (MSSA) bacteremia. We compared empiric β-lactams to vancomycin to assess clinical outcomes in patients with MSSA bacteremia. METHODS: We conducted a retrospective cohort study of adult inpatients with their first episode of MSSA bacteremia at two tertiary care hospitals in Vancouver, Canada, between 2007 and 2014. Exposure was either empiric β-lactam with or without vancomycin or vancomycin monotherapy. All patients received definitive treatment with cloxacillin or cefazolin. The primary outcome was 28-day mortality. Secondary outcomes were 90-day mortality, duration of bacteremia, and hospital length-of-stay. Outcomes were adjusted using multivariable logistic regression. RESULTS: Of 669 patients identified, 255 met inclusion criteria (β-lactam = 131, vancomycin = 124). Overall 28-day mortality was 7.06 % (n = 18). There were more cases of infective endocarditis in the β-lactam than in the vancomycin group [24 (18.3 %) vs 12 (9.7 %), p = 0.05]. Adjusted mortality at 28 days was similar between the two groups (OR 0.85; 95 % CI 0.27-2.67). The duration of bacteremia was longer in the vancomycin group (97.1 vs 70.7 h, p = 0.007). Transition to cloxacillin or cefazolin occurred within a median of 68.3 h in the vancomycin group. CONCLUSIONS: Empiric β-lactams was associated with earlier clearance of bacteremia by a median of 1 day compared to vancomycin. Future prospective studies are needed to confirm our findings.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".