The changing incidence of human papillomavirus-associated oropharyngeal cancer using multiple imputation from 2000 to 2010 at a Comprehensive Cancer Centre
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 it