Antibiotic synergy against <i>Staphylococcus aureus</i> : a systematic review and meta-analysis
Bibliographic record
Abstract
ABSTRACT Antimicrobial combinations have been extensively evaluated in vitro to identify synergistic combinations for clinical use. Despite the available literature, no studies comprehensively summarize the findings for antimicrobial combinations against Staphylococcus aureus . We performed a systematic review to identify synergistic combinations that may be beneficial for clinical use against S. aureus . The PubMed, Cochrane, and Web of Science databases were queried from inception to February 2024 for studies of in vitro assays evaluating two antimicrobials in combination against isolates of S. aureus . Studies were included if they used common methods to determine synergy including time-kill assays, checkerboard assays, or the combined gradient diffusion method. The proportion of isolates for which synergy was identified was compared for different antimicrobial combinations. Two hundred sixty-five studies were included for analysis. One hundred forty-two studies evaluated synergy against methicillin-resistant S. aureus (MRSA), 31 against methicillin-susceptible S. aureus (MSSA), and 92 assessed synergy against both MRSA and MSSA, or did not define the methicillin susceptibility profile of the isolates studied. Time-kill assays ( n = 176) and checkerboard assays ( n = 158) were the most frequently used methods, with few studies evaluating synergy using the combined gradient diffusion method ( n = 13). The proportion of synergy varied based on the antimicrobial combination and isolate being evaluated. Antimicrobial synergy has been extensively studied for S. aureus , with combinations of glycopeptides and cephalosporins being studied most frequently. Future evaluations of synergy for S. aureus should focus on antimicrobial combinations with strong rationales and robust potential for clinical use.
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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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".