The changing incidence of human papillomavirus-associated oropharyngeal cancer using multiple imputation from 2000 to 2010 at a Comprehensive Cancer Centre
Bibliographic record
Abstract
INTRODUCTION: Human papillomavirus (HPV) is a risk and prognostic factor for oropharyngeal cancer (OPC). Determining whether the incidence of HPV-associated OPC is rising informs health policy. METHODS: HPV status was ascribed using p16 immunohistochemistry in 683/1474 OPC patients identified from the Princess Margaret Hospital's Cancer Registry (from 2000 to 2010). Missing p16 data was estimated using multiple (n=100) imputation (MI) and validated using an independent OPC cohort (n=214). Non-OPC head and neck squamous cell carcinoma (HNSCC) (n=3262) were also used for time-trend comparison. Regression was used to compare HNSCC subsets and time-trends. The c-index was used to measure the predictive ability of MI. RESULTS: The incidence of OPC rose from 23.3% of all HNSCC in 2000 to 31.2% in 2010 (p=0.002). In the subset of OPC tested for p16, there was no change in p16 positivity over time (p=0.9). However, p16 testing became more frequent over time (p<0.0001), but was nonetheless biased, favouring never-smokers [OR 1.87 (95% CI 1.29-2.70)] and tumors of the tonsil [OR 2.30 (1.52-3.47)] or base-of-tongue [OR 1.72 (1.10-2.70)]. These same factors were also associated with p16-positivity [ORs 3.22 (1.27-8.16), 7.26 (3.50-15.1), 5.83 (2.70-12.7), respectively]. Following MI and normalization, the proportion of OPC that was p16-associated rose from 39.8% in 2000 to 65.0% in 2010, p=0.002, fully explaining the rise in OPC in our patient population. CONCLUSION: The rise in HNSCC referrals seen from 2000 to 2010 at our institution was driven primarily by p16-associated OPC. MI was necessary to derive reliable conclusions when cases with missing data are considerable.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| 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) | 0.002 | 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".