Few SARS-CoV-2 infections detected in Newfoundland and Labrador in the absence of Public Health Laboratory-based confirmation
Bibliographic record
Abstract
OBJECTIVE: To assess the incidence of COVID-19 infection in the absence of a confirmatory test in persons suspecting they contracted COVID-19 and elucidate reasons for their belief. METHODS: We recruited persons with a confirmed COVID-19 diagnosis and persons who believed they may have contracted COVID-19 between December, 2019 and April, 2021 into a study of immunity against SARS-CoV-2. An intake questionnaire captured their perceived risk factors for exposure and symptoms experienced, including symptom duration and severity. ELISA testing against multiple SARS-CoV-2 antigens was done to detect antibodies against SARS-CoV-2. No participant had received COVID-19 vaccination prior to the time of testing. RESULTS: The vast majority of study subjects without Public Health confirmation of infection had no detectable antibodies against SARS-CoV-2. Suspected infection with SARS-CoV-2 generally involved experiencing symptoms common to many other respiratory infections. Unusually severe or persistent symptoms often supported suspicion of infection with SARS-CoV-2 as did travel or contact with travelers from outside Newfoundland and Labrador. Rare cases in which antibodies against SARS-CoV-2 were detected despite negative results of Public Health testing for SARS-CoV-2 RNA involved persons in close contact with confirmed cases. CONCLUSIONS: Broad public awareness and declaration of pandemic status in March, 2020 contributed to the perceived risk of contracting COVID-19 in Newfoundland and Labrador from late 2019 to April 2021 and raised expectation of its severity. Serological testing is useful to diagnose past infection with SARS-CoV-2 to accurately estimate population exposure rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".