Oral lesions in young adults infected with COVID-19 and impact of smoking. A multi-country study (Preprint)
Bibliographic record
Abstract
<sec> <title>UNSTRUCTURED</title> 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. </sec>
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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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".