Rate of Second Primary Head and Neck Cancer With Cannabis Use
Bibliographic record
Abstract
Objective To determine whether there is an association between cannabis use and developing a second primary cancer in head and neck cancer patients, as well as determining the prevalence of cannabis use amongst head and neck cancer patients. Study design This retrospective cohort study investigated patients from the Hamilton Region Head and Neck Cancer Database who were enrolled prospectively between 2011 and 2015, with follow-up data up to November 2018. Patients were contacted to confirm current cannabis and tobacco smoking status. Setting All patients were enrolled from a single tertiary cancer center in Hamilton, Ontario. Subjects and methods Consecutive patients with a newly diagnosed head and neck cancer were prospectively enrolled between 2011 to 2015. Cannabis users and controls were compared using standard modes of comparison. The odds ratio from a multivariable logistic regression model was then determined. Results A total of 513 patients were included in this study: 59 in the cannabis group and 454 in the control group. In terms of baseline characteristics, there was no significant difference between cannabis users and controls except that cannabis users were more likely to develop primary oropharyngeal cancer (p=0.0046). Two of 59 (3.4%) cannabis users developed a second primary cancer, in comparison to 23 of 454 (5.1%) non-cannabis users. The odds ratio for cannabis use on the second primary cancer was 0.19 (95% CI [0.01-3.20], p=0.25). Conclusion This study suggests that cannabis use behaves differently than tobacco smoking, as the former may not be associated with field cancerization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".