The Emergency Medical Service Microbiome
Bibliographic record
Abstract
ABSTRACT Emergency medical services (EMS) personnel are an integral component of the health care framework and function to transport patients from various locations to and between care facilities. In addition to physical injury, EMS personnel are expected to be at high risk to acquire and transmit health care-associated infections (HAIs) in the workplace. However, currently, little is known about EMS biosafety risk factors and the epidemiological contribution of EMS to pathogen transmission within and outside the health care sector. Health care facility microbiomes contain diverse bacterial, fungal, and viral pathogens that cause over 1.7 million HAIs each year in the United States alone. While hospital microbiomes have been relatively well studied, there is scant information about EMS infrastructure and equipment microbiomes or the role(s) they play in HAI transmission between health care facilities. We review recent literature investigating the microbiome of ambulances and other EMS service facilities which consistently identify antibiotic-resistant pathogens causing HAIs, including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus , and Klebsiella pneumoniae . Our review provides evidence that EMS microbiomes are dynamic and important pathogen reservoirs, and it underscores the need for more widespread and in-depth microbiome studies to elucidate patterns of pathogen transmission. We discuss emerging DNA sequencing technologies and other methods that can be applied to characterize and mitigate EMS biosafety risks in the future. Understanding the complex interplay between EMS and hospital microbiomes will provide key insights into pathogen transmission mechanisms and identify strategies to minimize HAIs and community infection.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".