Beyond Genetics: Exploring Lifestyle, Microbiome, and Social Determinants in Oral Cancer Development
Bibliographic record
Abstract
Oral cancer refers to cancers originating in the oral cavity and oropharyngeal regions. It is the 16th most prevalent cancer and the sixth leading cause of cancer-related deaths. However, the mechanisms of its progression are still being understood, and interventions to provide early diagnosis need to be improved. More studies have recently been conducted on oral cancer, and many reviews have summarized the findings in this field, focusing on individual factors. However, few review articles have evaluated the combinational impacts of different factors on oral cancer. This review aimed to provide an overview of the combinational effects of three extracellular factors, including lifestyle habits, oral microbiome, and socioeconomic factors, on oral cancer progression. Oral cancer is differentially affected by lifestyle habits; high-sugar diets, processed foods, alcohol, smoking, and possibly sleep disorders benefit its progression, whereas eating natural diets, such as fruits, vegetables, fish, and garlic, drinking tea or coffee, and physical exercise can combat it. The oral microbiome could suppress or promote oral cancer progression. Low socioeconomic status can impact oral cancer development. Furthermore, crosstalk among these three factors affects oral cancer progression. This review has limitations in not including all oral cancer-affecting factors and all important publications. More focus should be placed on the combinational effects of multiple factors on oral cancer progression and treatment. The findings in this study could update researchers on the landscape of oral cancer progression and help formulate approaches to promote oral cancer prevention and treatment.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".