SARS-CoV-2 detection is independent of microbiome composition on surfaces in a major Ontario hospital
Bibliographic record
Abstract
Abstract The SARS-CoV-2 pandemic has resulted in considerable mortality in hospital settings. Built environment surveillance can provide a non-invasive indicator of SARS-CoV-2 status in hospitals, but we have a limited understanding of SARS-CoV-2’s microbial co-associations in the built environment, including any potential co-occurrence dynamics with pathogenic and antimicrobial-resistant microorganisms. Here we examine the microbial communities on floors and elevator buttons across several locations in two major tertiary-care Ontario hospitals during a surge in SARS-CoV-2 cases in 2020. Total microbial community composition, prevalence and type of detected antimicrobial resistance genes, and virulence factor distributions were governed by sample source rather than SARS-CoV-2 detection status. Fifteen microorganisms were identified as indicator species associated with positive SARS-CoV-2 signal, including three opportunistic pathogens (i.e., two Corynebacterium sp. and a Sutterella sp). Key clinically relevant antimicrobial resistance genes showed varying prevalence across sites within the hospital, suggesting that our workflow could inform resistance burden in hospitals. Overall, these results indicate limited or only weak interactions between microbiome composition and SARS-CoV-2 detection status in the hospital built environment.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".