Seroprevalence of SARS-CoV-2 antibodies in children of United Kingdom healthcare workers: a prospective multicentre cohort study protocol
Bibliographic record
Abstract
BACKGROUND: A novel coronavirus SARS-CoV-2 has been responsible for a worldwide pandemic. Children typically have very mild, or no, symptoms of infection. This makes estimations of seroprevalence in children difficult. Research is therefore required to determine the seroprevalence of SARS-CoV-2 antibodies in children. The primary objective of this study is to report the seroprevalence of SARS-CoV-2 IgM and/or IgG antibodies in healthy children at baseline, 2 months and 6 months. This is the only longitudinal UK study of seroprevalence in an exclusively paediatric population. Determining the changing seroprevalence is of vital public health importance and can help inform decisions around the lifting of paediatric specific social distancing measures such as school closures and the cancellation of routine paediatric hospital services. METHODS AND ANALYSIS: 1000 healthy children of healthcare workers aged between 2 and 15 years will be recruited from five UK sites (Belfast, Cardiff, Glasgow, London and Manchester). The children will undergo phlebotomy at baseline, 2 months and 6 months to measure IgM and/or IgG positivity to SARS-CoV-2. A sample size of 675 patients is required to detect a 5% change in seroprevalence at each time point assuming an alpha of 0.05 and a beta of 0.2. Adjusted probabilities for the presence of IgG and/or IgM antibodies and of SARS-CoV-2 infection will be reported using logistic regression models where appropriate. ETHICS AND DISSEMINATION: Ethical approval was obtained from the London - Chelsea Research Ethics Committee (REC Reference-20/HRA/1731) and the Belfast Health & Social Care Trust Research Governance (Reference 19147TW-SW). Results of this study will be made available as preprints and submitted for publication in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT0434740; Results.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".