A Telemedicine-Guided Self-Collection Approach for PCR-Based SARS-CoV-2 Testing: Comparative Study
Bibliographic record
Abstract
BACKGROUND: Large-scale, polymerase chain reaction (PCR)-based SARS-CoV-2 testing is expensive, resource intensive, and time consuming. A self-collection approach is a probable alternative; however, its feasibility, cost, and ability to prevent infections need to be evaluated. OBJECTIVE: This study aims to compare an innovative self-collection approach with a regular SARS-CoV-2 testing strategy in a large European industrial manufacturing site. METHODS: The feasibility of a telemedicine-guided PCR-based self-collection approach was assessed for 150 employees (intervention group) and compared with a regular SARS-CoV-2 testing approach used for 143 employees (control group). Acceptance, ergonomics, and efficacy were evaluated using a software application. A simulation model was implemented to evaluate the effectiveness. An interactive R shiny app was created to enable customized simulations. RESULTS: The test results were successfully communicated to and interpreted without uncertainty by 76% (114/150) and 76.9% (110/143) of the participants in the intervention and control groups, respectively (P=.96). The ratings for acceptability, ergonomics, and efficacy among intervention group participants were noninferior when compared to those among control group participants (acceptability: 71.6% vs 37.6%; ergonomics: 88.1% vs 74.5%; efficacy: 86.4% vs 77.5%). The self-collection approach was found to be less time consuming (23 min vs 38 min; P<.001). The simulation model indicated that both testing approaches reduce the risk of infection, and the self-collection approach tends to be slightly less effective owing to its lower sensitivity. CONCLUSIONS: The self-collection approach for SARS-CoV-2 diagnosis was found to be technically feasible and well rated in terms of acceptance, ergonomics, and efficacy. The simulation model facilitates the evaluation of test effectiveness; nonetheless, considering context specificity, appropriate adaptation by companies is required.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".