Diagnostic Yield of Screening for SARS-CoV-2 among Patients Admitted for Alternate Diagnoses
Bibliographic record
Abstract
Abstract Objectives To determine the diagnostic yield of screening patients for SARS-CoV-2 who were admitted with a diagnosis unrelated to COVID-19, and identify risk factors for positive tests. Design Cohort from the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry Setting 30 acute care hospitals across Canada Participants Patients hospitalized for non-COVID-19 related diagnoses who were tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) between March 1, and December 29, 2020 Main outcome Positive nucleic acid amplification test (NAAT) for SARS-CoV-2 Outcome measure Diagnostic yield Results We enrolled 15,690 consecutive eligible adults who were admitted to hospital without clinically suspected COVID-19. Among these patients, 122 tested positive for COVID-19, resulting in a diagnostic yield of 0.8% (95% CI 0.64% – 0.92%). Factors associated with a positive test included presence of a fever, being a healthcare worker, having a positive household contact or institutional exposure, and living in an area with higher 7-day average incident COVID-19 cases. Conclusions Universal screening of hospitalized patients for COVID-19 across two pandemic waves had a low diagnostic yield and should be informed by individual-level risk assessment in addition to regional COVID-19 prevalence. Trial registration NCT04702945 SUMMARY BOXES Section 1: Universal screening of admitted patients for SARS-CoV-2 was implemented in many hospitals at the beginning of the pandemic. The Infections Diseases Society of America (IDSA) recommended avoiding universal screening of asymptomatic hospitalized patients in areas and times of low-COVID prevalence (defined as <2% prevalence) with very low certainty of evidence, based on studies of COVID-19 prevalence among asymptomatic individuals in the community. Section 2: This study supports IDSA recommendations to avoid universal screening for COVID-19 in times and areas of low COVID prevalence and identifies patient-level risk factors strongly associated with positive testing that should be considered for screening.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".