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Record W3044264968 · doi:10.1017/ice.2020.349

Utility of asymptomatic inpatient testing for COVID-19 in a low-prevalence setting: A multicenter point-prevalence study

2020· article· en· W3044264968 on OpenAlexaff
Anthony D. Bai, Xena X. Li, Mohammed saud salem alsalem, Sarah Khan, Marek Smieja, Dominik Mertz, Zain Chagla

Bibliographic record

VenueInfection Control and Hospital Epidemiology · 2020
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsHamilton Health SciencesSt. Joseph’s Healthcare HamiltonMcMaster Children's HospitalMcMaster University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)MedicineAsymptomatic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Multicenter studyVirologyInternal medicineRandomized controlled trialDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.026
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.083
GPT teacher head0.430
Teacher spread0.347 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2020
Admission routes1
Has abstractyes

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