Antibiotic Resistance Trends Among Ocular Pathogens in the US—Cumulative Results from the Antibiotic Resistance Monitoring in Ocular Microorganisms (ARMOR) Surveillance Study
Bibliographic record
Abstract
Antibiotic resistance among ocular pathogens is a public health concern. The multicenter, prospective Antibiotic Resistance Monitoring in Ocular micRoorganisms (ARMOR) study is an ongoing surveillance study designed to report on antibiotic resistance rates and trends among Staphylococcus aureus , coagulase-negative staphylococci (CoNS; includes Staphylococcus epidermidis ), Streptococcus pneumoniae , Pseudomonas aeruginosa , and Haemophilus influenzae isolates from ocular infections. Results for more than 4,000 isolates collected from 2009 –2015, representing 7 years of ARMOR, were recently presented. More than a third of S. aureus and almost half of all CoNS isolates were found to be resistant to methicillin. Staphylococcal isolates also showed high levels of multidrug resistance (resistance to ≥3 antibacterial drug classes) with 76.4% and 73.7% of methicillin-resistant S. aureus (MRSA) and methicillin-resistant CoNS (MRCoNS) isolates, respectively, demonstrating multidrug resistance. Resistance among S. pneumoniae was notable for azithromycin (36.8%) and for penicillin (34.0%), whereas P. aeruginosa and H. influenzae were generally susceptible to the antibiotic classes tested. Longitudinal analyses demonstrated a small decrease in methicillin resistance among S. aureus over the 7-year study period, which may be a result of improved antibiotic stewardship. Continued surveillance of antibiotic resistance among ocular pathogens is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".