To Test, Perchance to Diagnose: Practical Strategies for Severe Acute Respiratory Syndrome Coronavirus 2 Testing
Bibliographic record
Abstract
Testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in symptomatic and asymptomatic patients is an important component of the multifaceted approach of managing the coronavirus disease 2019 pandemic. Determining how to best define testing strategies for different populations and incorporating these into broader infection prevention programs can be complex. Many circumstances are not addressed by federal, local, or professional guidelines. This commentary describes various scenarios in which testing of symptomatic or asymptomatic individuals for SARS-CoV-2 virus (antigen or ribonucleic acid) can be of potential benefit. Consideration to pretest probability, risks of testing (impact of false-positive or false-negative results), testing strategy, as well as action based on test results are explored. Testing, regardless of setting, must be incorporated into overarching infection control plans, which include use of personal protective equipment (eg, masks), physically distancing, and isolation when exposure is suspected.
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How this classification was reachedexpand
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 | 0.000 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".