Characterization of Ebola Virus Risk to Bedside Providers in an Intensive Care Environment
Bibliographic record
Abstract
BACKGROUND: The 2014-2016 Ebola outbreak in West Africa recapitulated that nosocomial spread of Ebola virus could occur and that health care workers were at particular risk including notable cases in Europe and North America. These instances highlighted the need for centers to better prepare for potential Ebola virus cases; including understanding how the virus spreads and which interventions pose the greatest risk. METHODS: We created a fully equipped intensive care unit (ICU), within a Biosafety Level 4 (BSL4) laboratory, and infected multiple sedated non-human primates (NHPs) with Ebola virus. While providing bedside care, we sampled blood, urine, and gastric residuals; as well as buccal, ocular, nasal, rectal, and skin swabs, to assess the risks associated with routine care. We also assessed the physical environment at end-point. RESULTS: Although viral RNA was detectable in blood as early as three days post-infection, it was not detectable in the urine, gastric fluid, or swabs until late-stage disease. While droplet spread and fomite contamination were present on a few of the surfaces that were routinely touched while providing care in the ICU for the infected animal, these may have been abrogated through good routine hygiene practices. CONCLUSIONS: Overall this study has helped further our understanding of which procedures may pose the highest risk to healthcare providers and provides temporal evidence of this over the clinical course of disease.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".