Diagnostic stewardship and the coronavirus disease 2019 (COVID-19) pandemic: Lessons learned for prevention of emerging infectious diseases in acute-care settings
Bibliographic record
Abstract
Executive summary The coronavirus disease 2019 (COVID-19) pandemic has demonstrated the importance of stewardship of viral diagnostic tests to aid infection prevention efforts in healthcare facilities. We highlight diagnostic stewardship lessons learned during the COVID-19 pandemic and discuss how diagnostic stewardship principles can inform management and mitigation of future emerging pathogens in acute-care settings. Diagnostic stewardship during the COVID-19 pandemic evolved as information regarding transmission (eg, routes, timing, and efficiency of transmission) became available. Diagnostic testing approaches varied depending on the availability of tests and when supplies and resources became available. Diagnostic stewardship lessons learned from the COVID-19 pandemic include the importance of prioritizing robust infection prevention mitigation controls above universal admission testing and considering preprocedure testing, contact tracing, and surveillance in the healthcare facility in certain scenarios. In the future, optimal diagnostic stewardship approaches should be tailored to specific pathogen virulence, transmissibility, and transmission routes, as well as disease severity, availability of effective treatments and vaccines, and timing of infectiousness relative to symptoms. This document is part of a series of papers developed by the Society of Healthcare Epidemiology of America on diagnostic stewardship in infection prevention and antibiotic stewardship. 1
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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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".