Utility of asymptomatic inpatient testing for COVID-19 in a low-prevalence setting: A multicenter point-prevalence study
Bibliographic record
Abstract
For coronavirus disease 2019 (COVID-19), frequently reported symptoms in nonseverely sick patients include fever, fatigue, and dry cough. 1 However, infected patients may not exhibit symptoms.Some patients may be presymptomatic and develop symptoms later in the disease course whereas others remain asymptomatic, but either group can be infectious.2,3 Hence, asymptomatic carriers and presymptomatic individuals may be potential sources of nosocomial transmission.As such, consideration can be given to testing asymptomatic patients upon admission to the hospital.The Infectious Diseases Society of America (IDSA) guidelines on the diagnosis of COVID-19 recommend against testing of asymptomatic hospitalized patients in lowprevalence (<2%) settings.4 This recommendation is based on expert opinion and lacks supporting evidence.The city of Hamilton, Ontario, Canada, has a population of 580,000 and qualified as a low-prevalence area at the time of this study.The average number of daily new cases identified was 1.9 per 100,000 population.5 For hospital admission, the testing strategy was (and continues to be) based on symptoms or exposures.6 Within this low-prevalence setting, we conducted a multicenter point-prevalence study to evaluate the utility of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing of asymptomatic patients in terms of capturing positive cases that would be missed by symptom-based testing on admission. MethodsWe conducted a point-prevalence study across 4 tertiary acute-care hospitals in Hamilton from April 15 to April 21, 2020.The Hamilton Integrated Research Ethics Board approved this study (no.10894). COVID-19 testing on admissionAccording to provincial guidelines, testing was based on the following symptoms: fever, new or worsening acute respiratory illness symptom (ie, cough, dyspnea, sore throat, runny nose or sneezing, nasal congestion, hoarse voice, difficulty swallowing, new
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".