Using rapid (point-of-care) tests for COVID-19: A decision analysis comparing the expected benefit of two screening strategies
Bibliographic record
Abstract
Abstract Background Rapid tests for COVID-19 could be used to augment the otherwise limited laboratory-based testing capacity, but there are concerns that their utility may be compromised by their limited accuracy. The objective of this article is to compare the expected benefit (EB) of two screening strategies, one with rapid tests (SwRT) and another one without rapid tests . Methods We performed a decision analysis, with the overall EB defined as the proportion of correctly identified individuals minus the proportion of incorrectly identified individuals. Accordingly, the SwRT strategy will be deemed a better screening strategy if its lesser EB for COVID-19 free individuals is more than compensated by its greater EB for COVID-19 individuals. Otherwise, it will not. Results As expected, the EB for COVID-19 individuals was greater for the SwRT strategy, with a far superior ability to rule out the presence of COVID-19. In fact, under the scenario of interest (i.e., 8000 ID Now rapid tests in addition to 28185 lab-based RT-PCR tests), it identified almost 16% more COVID-19 individuals than the strategy. In addition, the EB for COVID-19 free individuals was the same for both strategies, with a perfect ability at ruling in the presence of COVID-19. Conclusion The SwRT strategy identified more COVID-19 individuals and this gain was not obtained at the detriment of COVID-19 free individuals who were equally well identified by both strategies. Hence, the SwRT strategy is a better screening strategy for COVID-19. It represents an opportunity to curtail the spread of SARS-CoV-2 that we may not afford to miss with new more contagious variants becoming more and more common in Canada.
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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 | 0.045 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".