Marijuana and head and neck cancer: An epidemiological review
Bibliographic record
Abstract
BACKGROUND: Marijuana is the most widely used illicit substance in Canada. To date, no conclusive study has looked at the epidemiologic basis of marijuana use and head and neck cancer (HNC). Due to the imminent recreational legalization of marijuana in Canada, the epidemiologic relationship between marijuana use and HNC is becoming increasingly important. OBJECTIVE: To examine the epidemiologic characteristics of HNC patients who are recreational marijuana users. METHODS: This study was conducted at a single tertiary care centre from 2011 to 2014. Patients were enrolled consecutively at time of diagnosis of malignancy. Data was prospectively collected and included socioeconomic factors, alcohol/tobacco history, tumor characteristics, and treatment modality. Marijuana use was defined as current usage on an at least weekly basis. RESULTS: Eight hundred seventy-nine patients met inclusion and exclusion criteria. Seventy-four (8.4%) patients were classified as marijuana users. Compared to non-users, marijuana users were less likely to be married (p = 0.048) and had less significant tobacco smoking history (p = 0.004). There were no significant differences between other socioeconomic factors or local and regional disease (p > 0.05). Marijuana users differed in the proportion of cancers stratified by primary site (p < 0.0001), with higher rates of p16+ oropharyngeal cancers, and treatment modality (p < 0.0001), with more use of chemoradiation. CONCLUSIONS: HNC patients who were marijuana users were less likely to be married and smoke tobacco. They have a distinct cancer site prevalence and are more likely to be treated by chemoradiation. Understanding the epidemiological breakdown of marijuana users amongst HNC patients will be a useful adjunct for future studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".