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Record W3191298448 · doi:10.1101/2021.08.12.21261462

Field performance of NowCheck rapid antigen test for SARS-CoV-2 in Kisumu County, western Kenya

2021· preprint· en· W3191298448 on OpenAlexaff
Simon Onsongo, Kephas Otieno, Shannen van Duijn, Emily R. Adams, Mevis Omollo, Isdorah Odero, Alloys K’Oloo, Nathalie Houben, Emmanuel Milimo, Robert Aroka, Hellen C. Barsosio, Fredrick Oluoch, Albert Odhiambo, Simon Kariuki, Tobias F. Rinke de Wit

Bibliographic record

VenuemedRxiv · 2021
Typepreprint
Languageen
FieldMedicine
TopicSARS-CoV-2 detection and testing
Canadian institutionsCentre for Global Health Research
FundersPfizer FoundationAchmeaPfizer
KeywordsTurnaround timePoint-of-care testingMedicineKenyaSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AntigenGold standard (test)Point of careCoronavirus disease 2019 (COVID-19)Christian ministryVirologyInternal medicineImmunologyDiseaseNursingBiologyComputer scienceInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Abstract Background Low- and middle-income countries (LMICs) are increasingly adopting low-cost Coronavirus disease 2019 (COVID-19) rapid antigen tests to meet the high demand for SARS-CoV-2 testing. Whilst testing using real-time polymerase chain reaction (RT-PCR) is the current gold standard, its widespread use in LMICs is limited by high costs, turnaround times and is not readily available in most places. COVID-19 antigen tests (Ag-RDT) provide a suitable alternative due to their low cost, rapid turnaround time and easy to set up and use. This study aimed to assess the field performance of the NowCheck COVID-19 antigen kit (Ag-RDT) as a point of care test (POCT) in select healthcare facilities in western Kenya. Methods We conducted a prospective multi-facility field evaluation study of the NowCheck COVID-19 rapid antigen test (Ag-RDT) compared to SARS-CoV-2 RT-PCR (RT-PCR). After obtaining informed consent, trained laboratory technicians collected two pairs of oropharyngeal and nasopharyngeal swabs, both antigen and RT-PCR testing, first for Ag-RDT and next for RT-PCR. We performed Ag-RDTs on-site and shared the results with both the study participants and their healthcare providers within 15-30 minutes. We carried out all RT-PCR tests in a central referral laboratory. The turnaround time for RT-PCR results was typically 24-48 hours. We captured the results of both methods using an electronic digital application. Findings Between December 2020 and March 2021, we enrolled 997 participants who met the Kenyan Ministry of Health COVID-19 case definition. The median age of study participants was 39 years (range one to 80 years), with 54% male. Ag-RDT had a sensitivity of 84.5% (76.0-90.8) and a specificity of 94.4% (95% CI: 92.7-95.8) with an accuracy of 94.2% (92.5-95.6) when a cycle threshold value (Ct value) of ≤35 was used. The highest sensitivity of 87.7% (77.2-94.5) was observed in samples with Ct values ≤ 30 and the highest specificity of 97.5% (96.2-98.5) at Ct value of <40. Interpretation The NowCheck COVID-19 Ag-RDT showed good performance in field evaluation in multiple healthcare facilities in a developing country. The sensitivity of the kit exceeded the minimum recommended cut-off of 80% as recommended by WHO 1 . The high specificity of this kit at 94.4% at Ct values ≤33 and 97.5% at Ct values <40 matched that of real-time PCR, making it a good rule-out test for symptomatic patients with COVID-19-like symptoms. The faster turnaround time to results, lower cost, simple analytical steps requiring no equipment or infrastructure makes antigen testing an attractive field-screening method to meet the high demand for COVID-19 testing. Funding Achmea Foundation, Pfizer Foundation, Dimagi and the Netherlands Ministry of Foreign Affairs supported this project. The funding sources did not have any role in study design, data collection, analysis, interpretation, summarizing the data or decision to submit the manuscript for publication.

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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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.063
GPT teacher head0.330
Teacher spread0.266 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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