Roadmap to the Bioanalytical Testing of COVID-19: From Sample Collection to Disease Surveillance
Bibliographic record
Abstract
The disease caused by SARS-CoV-2, coronavirus disease 2019 (COVID-19), has led to a global pandemic with tremendous mortality, morbidity, and economic loss. The current lack of effective vaccines and treatments places tremendous value on widespread screening, early detection, and contact tracing of COVID-19 for controlling its spread and minimizing the resultant health and societal impact. Bioanalytical diagnostic technologies have played a critical role in the mitigation of the COVID-19 pandemic and will continue to be foundational in the prevention of the subsequent waves of this pandemic along with future infectious disease outbreaks. In this Review, we aim at presenting a roadmap to the bioanalytical testing of COVID-19, with a focus on the performance metrics as well as the limitations of various techniques. The state-of-the-art technologies, mostly limited to centralized laboratories, set the clinical metrics against which the emerging technologies are measured. Technologies for point-of-care and do-it-yourself testing are rapidly emerging, which open the route for testing in the community, at home, and at points-of-entry to widely screen and monitor individuals for enabling normal life despite of an infectious disease pandemic. The combination of different classes of diagnostic technologies (centralized and point-of-care and relying on multiple biomarkers) are needed for effective diagnosis, treatment selection, prognosis, patient monitoring, and epidemiological surveillance in the event of major pandemics such as COVID-19.
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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 | 0.032 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".