Infectious Diseases Society of America Guidelines on Infection Prevention for Healthcare Personnel Caring for Patients With Suspected or Known COVID-19 (July 2020)
Bibliographic record
Abstract
BACKGROUND: SARS-CoV-2 is a highly transmissible virus that can infect health care personnel and patients in health care settings. Specific care activities, in particular aerosol-generating procedures, may have a higher risk of transmission. The rapid emergence and global spread of SARS-CoV-2 has created significant challenges in health care facilities, particularly with severe shortages of personal protective equipment (PPE) used to protect health care personnel (HCP). Evidence-based recommendations for what PPE to use in conventional, contingency, and crisis standards of care are needed. Where evidence is lacking, the development of specific research questions can help direct funders and investigators. OBJECTIVE: Develop evidence-based rapid guidelines intended to support HCP in their decisions about infection prevention when caring for patients with suspected or known COVID-19. METHODS: IDSA formed a multidisciplinary guideline panel including front-line clinicians, infectious disease specialists, experts in infection control and guideline methodologists with representation from the disciplines of preventive care, public health, medical microbiology, pediatrics, critical care medicine and gastroenterology. The process followed a rapid recommendation checklist. The panel prioritized questions and outcomes. Then a systematic review of the peer-reviewed and grey literature was conducted. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach was used to assess the certainty of evidence and make recommendations. RESULTS: The IDSA guideline panel agreed on eight recommendations and provided narrative summaries of other interventions undergoing evaluations. CONCLUSIONS: Using a combination of direct and indirect evidence, the panel was able to provide recommendations for eight specific questions on the use of PPE for HCP providing care for patients with suspected or known COVID-19. Where evidence was lacking, attempts were made to provide potential avenues for investigation. There remain significant gaps in the understanding of the transmission dynamics of SARS-CoV-2 and PPE recommendations may need to be modified in response to new evidence.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
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".