Major Risk Factors in Head and Neck Cancer: A Retrospective Analysis of 12-Year Experiences
Bibliographic record
Abstract
BACKGROUND: Head and neck cancer (HNC) is the seventh most common type of cancer in the world and constitute 5% of the entire cancers worldwide. The global burden of HNC accounts for 650,000 new cases and 350,000 deaths worldwide every year and a major proportion of regional malignancies in India. More than 70% of squamous cell carcinoma of the head and neck are estimated to be avoidable by lifestyle changes, particularly by effective reduction of exposure to well-known risk factors such as tobacco smoking and alcohol drinking. METHODS: A retrospective analysis of 12 years (2001 - 2012) of HNC patients attending RCC, PGIMS Rohtak was done. Total numbers of cancer patients seen were 26,295 and out of these 9,950 patients were of HNCs, which were retrospectively analyzed for their associated risk factors in different HNC subtypes. Most of the patients, i.e. 92.3%, were presented as locally advanced HNC (stages III and IV). RESULTS: It has been observed that smoking and alcohol are the strongest independent risk factors responsible for increased risk of HNC and are further having synergetic correlations. CONCLUSION: The present study confirms the principal role of alcohol consumption and smoking in HNC carcinogenesis, as well as the differential associations with HNC subtypes, and a significant, positive, multiplicative interaction with different risk factors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".