Periodontal diseases and risk of oral cancer in Southern India: Results from the HeNCe Life study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Some studies suggest that periodontal diseases increase the risk of oral cancer, but contradictory results also exist. Inadequate control of confounders, including life course exposures, may have influenced prior findings. We estimate the extent to which high levels of periodontal diseases, measured by gingival inflammation and recession, are associated with oral cancer risk using a comprehensive subset of potential confounders and applying a stringent adjustment approach. In a hospital-based case-control study, incident oral cancer cases (N = 350) were recruited from two major referral hospitals in Kerala, South India, from 2008 to 2012. Controls (N = 371), frequency-matched by age and sex, were recruited from clinics at the same hospitals. Structured interviews collected information on several domains of exposure via a detailed life course questionnaire. Periodontal diseases, as measured by gingival inflammation and gingival recession, were evaluated visually by qualified dentists following a detailed protocol. The relationship between periodontal diseases and oral cancer risk was assessed by unconditional logistic regression using a stringent empirical selection of potential confounders corresponding to a 1% change-in-estimates. Generalized gingival recession was significantly associated with oral cancer risk (Odds Ratio = 1.83, 95% Confidence Interval: 1.10-3.04). No significant association was observed between gingival inflammation and oral cancer. Our findings support the hypothesis that high levels of periodontal diseases increase the risk of oral cancer.
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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 | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it