A Systematic Review of Multi-decade Antibiotic Resistance Data for Ocular Bacterial Pathogens in the United States
Bibliographic record
Abstract
INTRODUCTION: Since 2009, the Antibiotic Resistance Monitoring in Ocular Microorganisms (ARMOR) surveillance study has been assessing in vitro antibiotic resistance for bacterial isolates sourced from ocular infections in the US. The main goal of this systematic review was to compare in vitro resistance data for ocular pathogens from published US studies with the most recently published data from the ARMOR study (2009-2018) and, where possible, to evaluate trends in bacterial resistance over time over all studies. METHODS: databases (1/1/1995-6/30/2021). Data were extracted from relevant studies and antibiotic susceptibility rates for common ocular pathogens (Staphylococcus aureus, coagulase-negative staphylococci [CoNS], Streptococcus pneumoniae, Pseudomonas aeruginosa, and Haemophilus influenzae), longitudinal changes in susceptibility, and multidrug resistance (MDR) were compared descriptively. RESULTS: Thirty-two relevant studies were identified. High in vitro resistance was found among S. aureus and CoNS to fluoroquinolones, macrolides, and methicillin/oxacillin across studies, with high rates of MDR noted, specifically among methicillin-resistant staphylococci. Data from studies pre-dating or overlapping the early years of ARMOR reflected increasing rates of S. aureus resistance to fluoroquinolones, macrolides, methicillin/oxacillin, and aminoglycosides, while the ARMOR data suggested slight decreases in resistance to these classes between 2009 and 2018. Overall, methicillin-resistant S. aureus (MRSA) prevalence peaked from 2005 to 2015 with a possible decreasing trend in more recent years. DISCUSSION AND CONCLUSIONS: Data from local and regional US datasets were generally consistent with data from the national ARMOR surveillance study. Continued surveillance of ocular bacterial pathogens is needed to track trends such as methicillin resistance and MDR prevalence and any new emerging antibiotic resistance phenotypes. Susceptibility data from ARMOR can inform initial choice of therapy, especially in practice areas where local antibiograms are unavailable.
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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 | 0.011 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.028 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".