COVID-19 rapid diagnostic test could contain transmission in low- and middle-income countries
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has impacted heavily on global health. Although real-time polymerase chain reaction (RT-PCR) is the current diagnostic method, challenges for low- and middle-income countries (LMICs) necessitate cheaper, higher-throughput, reliable rapid diagnostic tests (RDTs). OBJECTIVE: We reviewed the documented performance characteristics of available COVID-19 RDTs to understand their public health utility in the ongoing pandemic, especially in resource-scarce LMIC settings. METHODS: Using a scoping review methodology framework, common literature databases and documentary reports were searched up to 22 April 2020, irrespective of geographical location. The search terms included 'SARS-CoV-2 AND serological testing' and 'COVID-19 AND serological testing'. RESULTS: A total of 18 RDTs produced in eight countries, namely China (6; 33.33%), the United States (4; 22.22%), Germany (2; 11.11%), Singapore (2; 11.11%), Canada, Kenya, Korea and Belgium (1 each; 5.56%), were evaluated. Reported sensitivity ranged from 18.4% to 100% (average = 84.7%), whereas specificity ranged from 90.6% to 100% (average = 95.6%). The testing time ranged from 2 min to 30 min. Of the 12 validated RDTs, the IgM/IgG duo kit with non-colloidal gold labelling system was reported to elicit the highest sensitivity (98% - 100%) and specificity (98% - 99% for IgG and 96% - 99% for IgM). CONCLUSION: We found reports of high sensitivity and specificity among the developed RDTs that could complement RT-PCR for the detection of SARS-CoV-2 antibodies, especially for screening in LMICs. However, it is necessary to validate these kits locally.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it