Evaluating diagnostic accuracy of an RT-PCR test for the detection of SARS-CoV-2 in saliva
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Saliva has been proposed as a potential more convenient, cost-effective, and easier sample for diagnosing SARS-CoV-2 infections, but there is limited knowledge of the impact of saliva volumes and stages of infection on its sensitivity and specificity. METHODS: In this study, we assessed the performance of SARS-CoV-2 testing in 171 saliva samples from 52 mostly mildly symptomatic patients (aged 18 to 70 years) with a positive reference standard result at screening. The samples were collected at different volumes (50, 100, 300, and 500 µl of saliva) and at different stages of the disease (at enrollment, day 7, 14, and 28 post SARS-CoV-2 diagnosis). Imperfect nasopharyngeal (NP) swab nucleic acid amplification testing was used as a reference. We used a logistic regression with generalized estimating equations to estimate sensitivity, specificity, PPV, and NPV, accounting for the correlation between repeated observations. RESULTS: The sensitivity and specificity values were consistent across saliva volumes. The sensitivity of saliva samples ranged from 70.2% (95% CI, 49.3-85.0%) for 100 μl to 81.0% (95% CI, 51.9-94.4%) for 300 μl of saliva collected. The specificity values ranged between 75.8% (95% CI, 55.0-88.9%) for 50 μl and 78.8% (95% CI, 63.2-88.9%) for 100 μl saliva compared to NP swab samples. The overall percentage of positive results in NP swabs and saliva specimens remained comparable throughout the study visits. We observed no significant difference in cycle number values between saliva and NP swab specimens, irrespective of saliva volume tested. CONCLUSIONS: The saliva collection offers a promising approach for population-based testing.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".