COVID-19 breakthrough infections in vaccinated participants of the Safety and Efficacy of Preventative COVID Vaccines sub-study
Bibliographic record
Abstract
BACKGROUND: The rate of breakthrough infection in vaccinated Ontarians during the Omicron wave is unknown. METHODS: Active participants of the Safety and Efficacy of Preventative COVID Vaccines (STOPCoV) study (892 ≥age 70 years and 369 aged 30-50 years) were invited to participate in a sub-study evaluating breakthrough COVID-19 infection. Self-administered rapid antigen tests (RAT) were reported twice weekly and symptom questionnaires weekly for 6 weeks. The primary outcome was the proportion reporting a positive RAT. RESULTS: A total of 806 e-consented, and 727 (90%) completed ≥1 RAT, with total 7,116 RATs completed between January 28 and March 29, 2022. Twenty out of twenty-five participants with a positive RAT had a booster vaccine prior to the positive test. All cases were mild, none requiring hospitalization. Nineteen had positive dried blood spot analysis for IgG antibody to the receptor binding domain (RBD) prior to the positive RAT. The mean normalized IgG ratio to RBD was 1.22 (SD 0.29) for younger and 0.98 (SD 0.44) for older participants, values similar to corresponding ratios for those without positive RATs and those in the main cohort. One hundred and five participants reported one and 96 reported ≥2 possible COVID-19 symptoms despite negative RATs. The false negative RAT was low (4% to 6.6 %) compared with subsequent positive nucleoprotein antibody. CONCLUSIONS: Positive RAT for COVID-19 was infrequent (3.4%). We were unable to determine a protective antibody level against breakthrough infection. Our findings can inform public health COVID-19 restrictions guidelines. Our decentralized study provides a model for rapid institution of new questions during a pandemic.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".