Survival outcomes of First Nations patients with oral cavity squamous cell carcinoma (Poliquin 2014)
Bibliographic record
Abstract
BACKGROUND: Oral cavity squamous cell carcinoma (OCSCC) is the most common head and neck cancer, affecting approximately 2000 Canadians yearly. Analysis of Canadian Cancer Registry data has shown that the incidence of oral cavity cancer is decreasing and survival outcomes are improving. There are significant health disparities in First Nations (FN) people in Canada. The incidence of cancer in FN groups is significantly lower when compared to the general population, but the cancer-related morbidity and mortality is significantly higher. There is no Canadian literature currently for OCSCC, or any other head and neck cancer, that compares survival outcomes of FN to the overall population. Therefore, the objective of this study is to determine whether there is a difference in epidemiology and survival outcomes between FN and non-FN patients with OCSCC. METHODS: This is a retrospective study of a population-based, prospectively-collected database from Alberta Cancer Registry (ACR). Patients with OCSCC, diagnosed and treated in Alberta between 1998 and 2009 were included. ACR data collected included patient gender, age at diagnosis, tobacco and alcohol use, FN status, TNM staging, performance status, date of death, cause of death, and follow-up. FN status was identified through the Alberta Health and Wellness registry and through postal code correlation for those who live on reserves. RESULTS: A total of 583 patients with OCSCC were included in this study. Of these, 19 were identified as being FN, leaving 564 non-FN patients. When comparing the FN and non-FN groups, there is no significant difference in baseline demographics. Estimated yearly incidences for OCSCC in the Alberta population (all ages) and FN patients are 1.74/100,000 and 1.32/100,000 respectively (p = 0.23). Significant differences are seen in overall survival (OS) (5-year OS 58.1% for non-FN and 33.7% for FN) and for disease-specific survival (DSS) (5-year DSS 67.8% for non-FN and 44.5% for FN). Multivariate analysis confirmed FN patients have a significant increase risk of death in OS and DSS, with hazard ratios of 4.20 (p = 0.01) and 4.57 (p = 0.02), respectively. CONCLUSIONS: The overall survival and disease specific survival are significantly lower in FN patients compared to non-FN patients with OCSCC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".