Demographic and Clinicopathologic Distribution of Oral Cavity and Oropharyngeal Cancer in Alberta, Canada: A Comparative Analysis.
Bibliographic record
Abstract
OBJECTIVES: The aims of this study were to determine demographic profiles, tumour characteristics and treatment factors related to oral cavity and oropharyngeal cancer (OCC and OPC) and comparatively analyze these cancers in the adult population of Alberta, Canada, over 12 years. METHODS: Demographic, tumour characteristics and treatment data regarding OCC and OPC incidence in Alberta residents ≥18 years in 2005-2017 were extracted from the Alberta Cancer Registry database. Age-standardized incidence and mortality rates (ASIR and ASMR) were computed. RESULTS: Among 3448 OCC and OPC cases, mean (standard deviation) age at diagnosis was 63.9 (14.4) and 60.1 (10.2) years, respectively. There was a male predilection for both OCC (58.2%) and OPC (81.7%). With some fluctuations, ASIR remained the same for OCC but increased for OPC. ASMR increased for both. The most common site for OCC was tongue and for OPC tonsil. Squamous cell carcinoma was the most common diagnosis for OCC and OPC. Involvement of at least 1 lymph node was observed in 38.5% of OCC and 85.8% of OPC cases. For 45.2% of OCC and 82.3% of OPC cases, diagnosis occurred at stage IV. The most common initial treatments for OCC were surgery, alone or combined with radiation, whereas radiation with chemotherapy was the main treatment modality for OPC. CONCLUSION: The incidence of OPC in younger males was higher than that of OCC. Although incidence of OPC per 100 000 population increased over the 12-year study period, it remained largely unchanged for OCC. For both cancers, initial diagnoses were made at advanced stages, with almost twice as many stage IV OPC cases than OCC cases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".