Epidemiological trends and survival of oropharyngeal cancer in a high <scp>HPV</scp> ‐prevalent area: A Danish population‐based study from 2000 to 2020
Bibliographic record
Abstract
Denmark, alongside other Scandinavian countries, the United States, Canada, and the United Kingdom, has high prevalence of human papillomavirus (HPV). Our oropharyngeal squamous cell carcinoma (OPSCC) database includes all diagnosed cases in Eastern Denmark during a period of more than two decades. We investigated the incidence, survival, and recurrence of patients with OPSCC with combined p16- and HPV testing covering a consecutive 21-year period. Age-adjusted incidence rate (AAIR) per 100,000, survival models, and Cox proportional-hazards model were employed. Two thousand eight hundred thirty-four patients were included (57.5% HPV positive (HPV+)/p16 positive (p16+), 33.7% HPV negative (HPV-)/p16 negative (p16-), 4% HPV+/p16-, and 4.8% HPV-/p16+). The AAIR for all patients increased from 1.8 to 5.1 per 100,000 from 2000 to 2020 linked to an increasing AAIR of HPV+/p16+ OPSCCs from 0.9 to 3.5 per 100,000 from 2000 to 2020. The AAIR for the HPV-/p16- OPSCCs decreased from 1.6 to 1.4 from 2017 to 2020. HPV+/p16+ OPSCCs had a higher 5-year overall survival (OS) of 79.2% compared to the other subgroups (HPV+/p16- OS: 50.4%; HPV-/p16+ OS: 49.4%; HPV-/p16- OS: 35.1%). The AAIR of the total OPSCC group increased from year 2000 to 2020, driven by a rise in the HPV+/p16+ group. A decreasing incidence rate was observed for the HPV-/p16- OPSCCs from 2017 to 2020. The OS for HPV+/p16+ OPSCCs was significantly higher compared to all other HPV/p16 subgroups. Therefore, we recommend testing for combined HPV and p16 status in patients with OPSCC when selecting patients for clinical trials, especially in case of de-escalating/escalating.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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 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".