COVID-19 rapid antigen testing and transmission risk associated with different activities or settings: Two living rapid reviews
Bibliographic record
Abstract
The key questions guiding these living rapid reviews are: KQ1: What is the effectiveness of different COVID-19 rapid testing strategies* including self-administered versus supervised testing, and different testing frequencies (e.g., one off compared to serial testing at different intervals for different lengths of time) at detecting infectiousness or reducing transmission? KQ2: What is the risk of COVID-19 transmission associated with different activities (e.g., dining, exercising etc.) or settings (e.g., educational, hospitality etc.) and what factors contribute to risk (e.g., type of contact, number of contacts, time within the risk environment)? KQ3: How does transmission risk of common activities alter with background population prevalence of SARS-CoV-2 and population vaccine coverage? *This review does not focus on the comparison of rapid antigen detection tests with a reference standard reverse transcription polymerase chain reaction (RT-PCR) test or the choice of individual tests. This research was funded by the Health Research Board through Evidence Synthesis Ireland [HRB Grant Number CBES-2018-001]. The living rapid reviews were also funded by The Strategy for Patient-Oriented Research Evidence Alliance (SPOR EA) which is supported by the Canadian Institutes of Health Research (CIHR) under the Strategy for Patient-Oriented Research (SPOR) initiative.
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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 | 0.049 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".