Burden and geographic distribution of oral cavity and oropharyngeal cancers in the Russian Federation
Bibliographic record
Abstract
Background: The global incidence of lip and oral cavity cancers (OCCs) and oropharyngeal cancers (OPCs) is steadily increasing. While tobacco and alcohol consumption are established risk factors, a considerable proportion of these cancers has become attributed to human papilloma virus (HPV) infection. We aimed to describe the occurrence and identify potential risk factors of OCCs and OPCs across the Russian Federation during 2007-2018. Methods: We conducted an ecological analysis using publicly accessible data from the P.A. Herzen Moscow Oncology Research Institute. Incidence and mortality rates by jurisdiction were mapped for geospatial analysis. We pre-defined 11 potential contributing risk factors and used univariable and multivariable Poisson regression model with backwards stepwise variable selection to identify associated factors with OCC and OPC. Results: A total of 190,585 individuals were diagnosed with OCCs and OPCs in Russia between 2007-2018. Non-uniform geographic distribution of cancer cases was noted where the Far Eastern Federal District had the highest rate of OCC and the Central Federal District of OPCs. Districts with high weekly alcohol consumption had significantly higher incidence and mortality rates in both sexes. Districts with high rates of daily smoking had higher incidence of OCC among females, and those with low smoking trends had lower mortality rates for OCCs and OPCs. Conclusion: We detail the burden of OCCs and OPCs across Russia, with the aim of elucidating modifiable risk factors and proposing evidence-based prevention strategies. Tobacco/alcohol sales control measures and smoking/drinking cessation programs should continue to be prioritized as public health measures, especially for females.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".