Oral Mucosa, Saliva, and COVID-19 Infection in Oral Health Care
Bibliographic record
Abstract
The SARS-CoV-2 virus has shaken the globe with an ongoing pandemic of COVID-19 and has set challenges to every corner of the modern health care setting. The oral mucosa and saliva are high risk sites for higher viral loads and dental health care professionals are considered a high risk group. COVID-19-induced oral lesions and loss of taste and smell are common clinical complaints in the dental health care setting. The SARS-CoV-2 virus has been found to cause a wide range of non-specific oral mucosal lesions, but the specific diagnosis of these mucocutaneous lesions as COVID-19 lesions will facilitate the prevention of SARS-CoV-2 in dental health care settings and aid in proper patient management. The reported loss of taste and smell needs further investigation at the receptor level as it will give new insights into SARS-CoV-2 pathogenicity. The high yield of virus in the salivary secretion is a common finding in this infection and ongoing research is focusing on developing saliva as a rapid diagnostic fluid in COVID-19. In this review, we discuss the significance of oral mucosa, saliva and the relevance of the COVID-19 pandemic in dentistry.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".