Urban and rural differences in outcomes of head and neck cancer
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To assess for potential urban and rural disparities in head and neck cancer (HNC) outcomes within a single-payer healthcare system. STUDY DESIGN: A large retrospective population-based cohort analysis of consecutive HNC patients treated in British Columbia, Canada between 2001 and 2010 was conducted. METHODS: All patients diagnosed with HNC from 2001 to 2010 and referred to any one of five British Columbia Cancer Agency centers for management were reviewed. Based on census data, patients were classified into: 1) rural, 2) small urban, 3) moderate urban, and 4) large urban areas. Kaplan-Meier methods and Cox regression models were used to correlate site of residence with overall survival (OS), controlling for prognostic factors that included sociodemographic and other tumor and treatment-related characteristics. RESULTS: We identified 3,036 patients; the median age was 64 years, 26% were women, and 32% had Eastern Cooperative Oncology Group (ECOG) 0 or 1. The majority resided in large urban areas (55%) followed by rural (22%), moderate urban (13%), and small urban (10%). In regression analyses, smoking (hazard ratio [HR]: 2.10, 95% confidence interval [CI]: 1.28-3.45, P < .001), ECOG 2 + (HR: 3.44, 95% CI: 2.26-5.22, P < .001), oral cavity (HR: 1.54, 95% CI: 1.03-2.32, P = .04) and hypopharyngeal tumors (HR: 2.31, 95% CI: 1.42-3.77, P = .00), and large tumor size (HR: 1.69, 95% CI: 1.08-2.64, P = .02) were correlated with inferior OS, but site of residence was not. When stratified by type of treatment, OS remained similar irrespective of urban or rural residence. CONCLUSIONS: Urban-rural differences in HNC survival outcomes were not observed. LEVEL OF EVIDENCE: 2c. Laryngoscope, 128:852-858, 2018.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".