Is the glass half full? Extraction-free RT-LAMP to detect SARS-CoV-2 is less sensitive but highly specific compared to standard RT-PCR in 101 samples
Bibliographic record
Abstract
Abstract The current scale of public and private testing cannot be expected to meet the emerging need for higher levels of community-level and repeated screening of asymptomatic Canadians for SARS-CoV-2. Rapid point-of-care techniques are increasingly being deployed to fill the gap in screening levels required to identify undiagnosed individuals with high viral loads. However, rapid, point-of-care tests often have lower sensitivity in practice. Reverse transcription loop-mediated isothermal amplification (RT-LAMP) for SARS-CoV-2 has proven sensitive and specific and provides visual results in minutes. Using a commercially available kit for RT-LAMP and primer set targetting nucleocapsid (N) gene, we tested a blinded set of 101 archived nasopharyngeal (NP) swab samples with known RT-PCR results. RT-LAMP reactions were incubated at 65°C for 30 minutes, using heat-inactivated nasopharyngeal swab sample in viral transport medium, diluted tenfold in water, as input. RT-LAMP agreed with all RT-PCR defined negatives (N=51), and all positives with Ct less than 20 (N=24), 65% of positives with Ct between 20-30 (N=17), and no positives with Ct greater than 30 (N=9). RT-LAMP requires fewer and different core components, so may not compete directly with the mainline testing workflow, preserving precious central laboratory resources and gold standard tests for those with the greatest need. Careful messaging must be provided when using less-sensitive tests, so that people are not falsely reassured by negative results – “glass half empty” – in exchange for reliable detection of those with high levels of virus within an hour, using <$10 worth of chemicals – “glass half full”.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".