Changes in the proportion of squamous cell carcinoma in head and neck cancer in the United States and Canada, 1995-2015.
Bibliographic record
Abstract
e17554 Background: There has been a shift in the epidemiologic landscape of head and neck cancer (HNC) with decreasing incidence of tobacco-related and increasing incidence of human papillomavirus (HPV)-related HNC. While it is often reported that ≥ 90% of HNC is considered squamous cell carcinoma (SCC), there is an apparent lack of recent population-based data to support this claim. This study aimed to estimate the current proportion and evaluate change in the proportion of SCC in HNC diagnoses in North America (United States and Canada) from 1995 to 2015. Methods: We queried the North American Association of Central Cancer Registries (NAACCR) database for HNC cases that were of either squamous (SQ) (ICD-0-3: 8050-8089) or squamous plus unspecified epithelial (SQE) (ICD-0-3:8010-8089) origin in the United States and Canada ( n = 1,054,409). All HNC included in the analysis were microscopically confirmed, malignant head and neck primary tumor sites of the oral cavity, nasopharynx, hypopharynx, oropharynx, nasal cavity, and larynx. Sub-analyses were conducted across more extensive cohort restriction combinations (country specific, registry specific, and primary sequence of cancer). Results: The overall proportion of SCC in HNC in North America from 1995-2015 was 81.7% (95% CI: 81.7 – 81.8) for SQ and 84.9% (95% CI: 84.8 – 85.0) for SQE. The proportion of SCC in HNC peaked in 2015 with 83.3% (95% CI: 83.0 – 83.6) for SQ and 85.9% (95% CI: 85.6 – 86.2) for SQE; and was lowest in 2005 with 80.7% (95% CI: 80.4 – 81.1) for SQ and 84.3% (95% CI: 83.9 – 84.6) for SQE. In the time period of this study (1995 – 2015), there were no years for which SQ or SQE made up 90% or more of HNC for any of the HNC cohorts. Conclusions: The changing landscape of HNC risk factors in the United States and Canada warrants re-evaluation and update of HNC epidemiological literature with regards to the proportion of SCC in HNC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 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".