Diagnostic yield of serial SARS-CoV-2 testing in hospitalized patients
Bibliographic record
Abstract
BACKGROUND: The detection rate of SARS-CoV-2 by polymerase chain reaction (PCR) varies depending on the time since exposure and is highest around the time of symptom onset. It is conceivable that patients who are incubating SARS-CoV-2 may screen negative at admission and develop transmissible but undetected asymptomatic or pre-symptomatic disease while in hospital. The incidence of COVID-19 in Montreal, Canada started to increase in December 2020. In anticipation of a much larger rise after the holiday period, the McGill University Health Centre implemented serial SARS-CoV-2 testing for all admitted patients on day 5 and 10 after admission, to evaluate the clinical utility of serial SARS-CoV-2 testing among patients who test negative on admission screening. METHODS: We retrospectively analyzed the diagnostic yield of SARS-CoV-2 serial testing for patients admitted between January 4, 2021 and April 30, 2021. RESULTS: A total of 1,505 patients underwent serial testing at day 5 and 841 patients underwent serial testing at day 10. Only 10 patients were positive on serial testing at day 5 and only 12 patients were positive on serial testing at day 10, for a yield at day 5 and day 10 of 0.7% and 1.4%, respectively. CONCLUSIONS: The yield of serial SARS-CoV-2 testing was 0.7% at day 5 and 1.4% at day 10. We found that the yield of serial testing was higher when the community incidence was higher and could be considered in this situation. Policies which target repeat testing towards symptomatic or exposed individuals appear to be effective in identifying those with a positive test while admitted but testing negative upon admission.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".