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Record W4213341175 · doi:10.1101/2022.02.10.22270805

Adequacy of serial self-performed SARS-CoV-2 rapid antigen-detection testing for longitudinal mass screening in the workplace

2022· preprint· en· W4213341175 on OpenAlexafffundabout
Jesse Papenburg, Jonathon R. Campbell, Chelsea Caya, Cynthia Dion, Rachel Corsini, Matthew P. Cheng, Dick Menzies, Cédric P. Yansouni

Bibliographic record

VenuemedRxiv · 2022
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsMcGill UniversityInstitute of Infection and ImmunityMcGill University Health CentreMontreal Children's Hospital
FundersFonds de Recherche du Québec - SantéFondation familiale Trottier
KeywordsAsymptomaticMedicineTest (biology)Surgery

Abstract

fetched live from OpenAlex

ABSTRACT Importance Longitudinal mass testing using rapid antigen detection tests (RADT) for serial screening of asymptomatic persons has been proposed for preventing SARS-CoV-2 community transmission. The feasibility of this strategy relies on implementation of accurate self-performed RADT testing where people live, work, or attend school. Objective To quantify the adequacy of serial self-performed SARS-CoV-2 RADT testing in the workplace, in terms of the frequency of correct execution of procedural steps and accurate interpretation of the range of possible RADT results. We compared results using the instructions provided by the manufacturer to those with modified instructions that were informed by the most frequent or most critical errors we observed. Design Repeated cross-sectional, diagnostic accuracy study performed prospectively in the field. Setting Businesses in Montreal, Quebec, Canada, with at least 2 active cases of SARS-CoV-2 infection. Participants Untrained, asymptomatic persons in their workplace, not meeting Public Health quarantine criteria. Exposures A Modified Quick Reference Guide compared to the original manufacturer’s instructions. Main Outcome(s) and Measure(s) The difference in the proportions of correctly performed procedural steps, and the difference in proportions of correctly interpreted RADT proficiency panel results. The secondary outcome, among subjects with two self-testing visits, compared the second to the first self-test visit using the same measures. Results Overall, 1892 tests were performed among 647 subjects. For self-test visit 1, significantly better accuracy in test interpretation was observed using the Modified Quick Reference Guide for weak positive (55.6% vs. 12.3%; 43.3 percentage point improvement, 95% confidence interval [CI] 33.0%-53.8%), positive (89.6% vs. 51.5%; 38.1% difference, 95%CI 28.5%-47.5%), strong positive (95.6% vs. 84.0%; 11.6% improvement, 95%CI 6.8%-16.3%) and invalid (87.3% vs. 77.3%; 10.0% improvement, 95%CI 3.8%-16.3%) tests. Use of the modified guide was associated with smaller, statistically significant, improvements on self-test visit 2. For procedural steps identified as critical for the validity of test results, adherence to procedural testing steps did not differ meaningfully according to instructions provided or reader experience. Conclusions and Relevance Longitudinal mass RADT testing for SARS-CoV-2 can be accurately self-performed in an intended-use setting; this work provides evidence for how to optimise performance. Key Points Questio Do untrained users correctly perform and interpret the results of SARS-CoV-2 rapid antigen detection tests (RADT) in the workplace, and how can their performance be optimised? Findings In this prospective field evaluation of self-performed SARS-CoV-2 RADT in an intended-use setting, we found that the accuracy of RADT interpretation was poor when the manufacturer’s instructions were used. A Modified Quick Reference Guide yielded significantly better user performance. Meaning Longitudinal mass RADT testing for SARS-CoV-2 can be accurately self-performed in an intended-use setting; this work provides evidence for how to optimise performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.338
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2022
Admission routes3
Has abstractyes

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