A revisit at 16 years for individuals from peri-urban New Delhi for tobacco use and associated oral lesions
Bibliographic record
Abstract
Objectives: India has a high incidence of oral cancer due to multifarious tobacco use. The objective of this study was to assess the status of tobacco-related oral lesions over 16 years, in a screen-detected population. Methods: This cross-sectional study involved home visits of 2000 Delhi residents, previously screened for oral potentially malignant disorders/oral cancer and counseled for tobacco cessation. Their basic demographics and tobacco/alcohol history were noted followed by oral visual examination for any related mucosal abnormalities. The data thus obtained were statistically analyzed. Results: Two hundred and sixty-five individuals (13.2%) could be traced after 16 years. The status of oral lesions varied across the participants, mainly in terms of their location, type, number, and/or presence/absence; no oral malignancies were noted. Most individuals had either a decreased use (34%, p < 0.001) or had quit tobacco (25.7%, p < 0.001); 8.3% individuals from the former and 5.7% from the latter group showed complete lesion(s) regression. The overall change in the tobacco use and oral lesions showed a highly significant positive association ( p < 0.05). Conclusion: A direct relationship exists between tobacco use and oral lesions. Repeated, tobacco cessation counseling provided by health-care professionals is effective. Oral screening of high-risk individuals, along with tobacco cessation, is thus essential.
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 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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".