One-Year Update on Salivary Diagnostic of COVID-19
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is a global health problem, which is challenging healthcare worldwide. In this critical review, we discussed the advantages and limitations in the implementation of salivary diagnostic platforms of COVID-19. The diagnostic test of COVID-19 by invasive nasopharyngeal collection is uncomfortable for patients and requires specialized training of healthcare professionals in order to obtain an appropriate collection of samples. Additionally, these professionals are in close contact with infected patients or suspected cases of COVID-19, leading to an increased contamination risk for frontline healthcare workers. Although there is a colossal demand for novel diagnostic platforms with non-invasive and self-collection samples of COVID-19, the implementation of the salivary platforms has not been implemented for extensive scale testing. Up to date, several cross-section and clinical trial studies published in the last 12 months support the potential of detecting SARS-CoV-2 RNA in saliva as a biomarker for COVID-19, providing a self-collection, non-invasive, safe, and comfortable procedure. Therefore, the salivary diagnosis is suitable to protect healthcare professionals and other frontline workers and may encourage patients to get tested due to its advantages over the current invasive methods. The detection of SARS-CoV-2 in saliva was substantial also in patients with a negative nasopharyngeal swab, indicating the presence of false negative results. Furthermore, we expect that salivary diagnostic devices for COVID-19 will continue to be used with austerity without excluding traditional gold standard specimens to detect SARS-CoV-2.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".