Oral lesions in young adults infected with COVID-19 and impact of smoking. A multi-country study (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED Objectives: To assess the reported presence of oral lesions in COVID-19-infected young adults and the difference between smokers and non-smokers in this association. Methods: This cross-sectional multi-country study recruited 18-to-23 year-old adults using an electronic validated questionnaire assessing COVID-19-infection, smoking and the presence of oral lesions/conditions (dry mouth, change in taste, and others). Multi-level logistic regression assessed the association between oral lesions and COVID-19 infection, and how smoking modified the associations between COVID-19 and oral lesions/conditions. Results: Data was available from 5342 respondents from 43 countries. Of these, 8.1% reported COVID-19-infection, 42.7% had oral lesions and 12.3% were smokers. A significantly greater percentage of COVID-19-infected participants reported dry mouth and change in taste than non-infected persons. Smokers had significantly higher odds of stained teeth with COVID-19 infection than non-smokers (AOR: 1.24 and 1.00; p=0.02). The association between COVID-19-infection and dry mouth was stronger among smokers than non-smokers (AOR=1.26 and 1.03, p=0.09) while the association with change in taste was stronger among non-smokers (AOR=1.22 and 1.13, p= 0.86). Conclusion: Dry mouth and changed taste were associated with COVID-19-infection and may be used to screen for COVID-19 in low COVID-19-testing environments. Smoking may modify the association between some oral lesions and COVID-19-infection.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".