Differences in Oral Lesions Associated with Tobacco Smoking, E-Cigarette Use and COVID-19 Infection among Adolescents and Young People in Nigeria
Bibliographic record
Abstract
COVID-19 infection is associated with oral lesions which may be exacerbated by tobacco smoking or e-cigarette use. This study assessed the oral lesions associated with the use of e-cigarettes, tobacco smoking, and COVID-19 among adolescents and young people in Nigeria. A national survey recruited 11–23-year-old participants from the 36 States of Nigeria and the Federal Capital Territory, Abuja. Data were collected using Survey Monkey®. Binary logistic regression analysis was conducted. Statistical significance was set at p-value less than 0.05. There were 2870 participants, of which 386 (13.4%) were tobacco smokers, 167 (5.8%) e-cigarette users, and 401 (14.0%) were both e-cigarette and tobacco users; and 344 (12.0%) had ever tested positive to COVID-19. Adolescents and young people who smoked tobacco had more than twice the odds of reporting gingival inflammation, oral ulcers, dry mouth, and changes in taste than those who did not smoke. Those who used e-cigarettes had 1.5 times higher odds of reporting oral lesions. Respondents who had COVID-19 infection had higher odds of reporting gingival inflammation and lower odds of reporting dry mouth than those who did not have COVID-19 infection. These findings were significant, and may help clinicians to screen for tobacco use and COVID-19 among adolescents and young people in Nigeria.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".