MétaCan
Menu
Back to cohort

Sensitivity and Diagnostic Yield of the First SARS-CoV-2 Nucleic Acid Amplification Test Performed for Patients Presenting to the Hospital

2022· article· en· W4304695270 on OpenAlexaffabout
Corinne M. Hohl, Jeffrey P. Hau, Samuel Vaillancourt, Jennifer Grant, Steven C. Brooks, Laurie J. Morrison, Jeffrey J. Perry, Rhonda J. Rosychuk, Ian B.K. Martin, John Taylor, Maja Stachura, Frank Scheuermeyer, Daniel K. Ting, Baljeet Brar, Lee Graham, I. V. Mitchell, Andrew D. McRae, Brian Row, Jake Hayward, Jaspreet Khangura, Philip J. Davis, Michelle Welsford, Robert Ohle, Justin W. Yan, Ivy Cheng, Megan Landes, Rohit Mohindra, Patrick Archambault, Joel Turner, Lars Grant, Éric Mercier, Gregory Clark, Raoul Daoust, Sébastien Robert, Kavish Chandra, Patrick T. Fok, Hana Wiemer, Sam Campbell, Kory Arsenault

Bibliographic record

VenueJAMA Network Open · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsUniversity of AlbertaOttawa HospitalUniversity of OttawaCanadian Respiratory Research NetworkQueen's UniversityUniversity of TorontoVancouver Coastal HealthVancouver Coastal Health Research InstituteUniversity of British ColumbiaSt. Michael's Hospital
Fundersnot available
KeywordsMedicineEmergency departmentNucleic Acid Amplification TestsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Diagnostic testCoronavirus disease 2019 (COVID-19)Emergency medicineInternal medicinePediatricsVirologyInfectious disease (medical specialty)Disease

Abstract

fetched live from OpenAlex

Importance: Early and accurate diagnostic testing for SARS-CoV-2 is essential to initiate appropriate treatment and infection control and prevention measures among patients presenting to the hospital. Objective: To evaluate the diagnostic sensitivity of the SARS-CoV-2 nucleic acid amplification test (NAAT) performed within 24 hours of arrival to the emergency department among a nationally representative sample of patients. Design, Setting, and Participants: This diagnostic study was conducted at 47 hospitals across 7 provinces in Canada participating in the Canadian COVID-19 Rapid Response Emergency Department Network among consecutive eligible patients presenting to a participating emergency department who were tested for SARS-CoV-2 from March 1, 2020, to December 31, 2021. Patients not tested within 24 hours of arrival and those presenting with a positive result from a test performed in the community were excluded. Main Outcomes and Measures: The primary outcome was a positive result from the SARS-CoV-2 NAAT. Outcome measures were the diagnostic sensitivity and yield of the SARS-CoV-2 NAAT. Results: Of 132 760 eligible patients (66 433 women [50.0%]; median age, 57 years [IQR, 37-74 years]), 17 174 (12.9%) tested positive for SARS-CoV-2 within 14 days of their first NAAT. The diagnostic sensitivity of the SARS-CoV-2 NAAT was 96.2% (17 070 of 17 740 [95% CI, 95.9%-96.4%]) among all of the tests performed. Estimates ranged from a high of 97.7% (1710 of 1751 [95% CI, 96.8%-98.3%]) on day 2 of symptoms to a low of 90.4% (170 of 188 [95% CI, 85.3%-94.2%]) on day 11 of symptoms among patients presenting with COVID-19 symptoms. Among patients reporting COVID-19 symptoms, the sensitivity of the SARS-CoV-2 NAAT was 97.1% (11 870 of 12 225 [95% CI, 96.7%-97.3%]) compared with 87.6% (812 of 927 [95% CI, 85.2%-89.6%]) among patients without COVID-19 symptoms. The diagnostic yield of the SARS-CoV-2 NAAT was 12.0% (18 985 of 158 004 [95% CI, 11.8%-12.2%]) and varied from a high of 20.0% (445 of 2229 [95% CI, 18.3%-21.6%]) among patients tested on day 10 after symptom onset to a low of 8.1% (1686 of 20 719 [95% CI, 7.7%-8.5%]) among patients presenting within the first 24 hours of symptom onset. Conclusions and Relevance: This study suggests that the diagnostic sensitivity was high for the first SARS-CoV-2 NAAT performed in the hospital and did not vary significantly by symptom duration. Repeated testing of patients with negative test results should be avoided unless their pretest probability of disease is high.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation 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 score0.025
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.279
Teacher spread0.246 · 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 teacher head, 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
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueJAMA Network OpenSame topicSARS-CoV-2 detection and testingFrench-language works237,207