Lessons from a rapid systematic review of early SARS-CoV-2 serosurveys
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background. As the world grapples with the COVID-19 pandemic, there is increasing global interest in the role of serological testing for population monitoring and to inform public policy. However, limitations in serological study designs and test standards raise concerns about the validity of seroprevalence estimates and their utility in decision-making. There is now a critical window of opportunity to learn from early SARS-CoV-2 serology studies. We aimed to synthesize the results of SARS-CoV-2 serosurveillance projects from around the world and provide recommendations to improve the coordination, strategy, and methodology of future serosurveillance efforts. Methods. This was a rapid systematic review of cross-sectional and cohort studies reporting seroprevalence outcomes for SARS-CoV 2. We included completed, ongoing, and proposed serosurveys. The search included electronic databases (PubMed, MedRXIV, BioRXIV, and WHO ICTPR); five medical journals (NEJM, BMJ, JAMA, The Lancet, Annals of Internal Medicine); reports by governments, NGOs, and health systems; and media reports (Google News) from December 1, 2019 to May 1, 2020. We extracted data on study characteristics and critically appraised prevalence estimates using Joanna Briggs Institute criteria. Results. Seventy records met inclusion criteria, describing 73 studies. Of these, 23 reported prevalence estimates: eight preprints, 14 news articles, and one government report. These studies had a total sample size of 35,784 and reported 42 prevalence estimates. Seroprevalence estimates ranged from 0.4% to 59.3%. No estimates were found to have a low risk of bias (43% high risk, 21% moderate risk, 36% unclear). Fifty records reported characteristics of ongoing or proposed serosurveys. Overall, twenty countries have completed, ongoing, or proposed serosurveys. Discussion. Study design, quality, and prevalence estimates of early SARS-CoV2 serosurveys are heterogeneous, suggesting that the urgency to examine seroprevalence may have compromised methodological rigour. Based on the limitations of included studies, future serosurvey investigators and stakeholders should ensure that: i) serological tests used undergo high-quality independent evaluations that include cross-reactivity; ii) all reports of serosurvey results, including media, describe the test used, sample size, and sampling method; and iii) initiatives are coordinated to prevent test fatigue, minimize redundant efforts, and encourage better study methodology. Other. PROSPERO: CRD42020183634. No third-party funding.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it