Automated molecular testing of saliva for SARS-CoV-2 detection
Bibliographic record
Abstract
Abstract With surging global demand for increased SARS-CoV-2 testing capacity, clinical laboratories seek automated, high-throughput molecular solutions, particularly for specimen types which do not rely upon supply of specialized collection devices or viral transport media (VTM). Saliva was evaluated as a diagnostic specimen for SARS-CoV-2 using the cobas® SARS-CoV-2 Test on the cobas® 6800 instrument. Saliva specimens submitted from various patient populations under investigation for COVID-19 from March-July 2020 were processed in the laboratory with sterile phosphate-buffered saline in a 1:2 dilution and vortexed with glass beads. The processed saliva samples were tested using a commercial assay for detection of the SARS-CoV-2 E gene (LightMix®) in comparison to the cobas® SARS-CoV-2 Test. 22/64 (34.4%) of the saliva samples were positive for SARS-CoV-2. Positive and negative concordance between the LightMix® and cobas® assays were 100%. There was no cross-contamination of samples observed on the cobas® 6800. The overall invalid rate for saliva on the cobas® 6800 (1/128, 0.78%) was similar to the baseline invalid rate observed for nasopharyngeal swabs/VTM and plasma samples. Saliva is a feasible specimen type for SARS-CoV-2 testing on the cobas® 6800, with potential to improve turnaround time and enhance testing capacity.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".