Urban and rural differences in outcomes of head and neck cancer (HNC).
Bibliographic record
Abstract
6570 Background: Management of HNC is becoming more specialized where effective treatments frequently require multidisciplinary and multimodality care. Concerns exist that access to such complex care may be suboptimal for marginalized subsets of the population. Our aim was to examine for potential urban and rural disparities in HNC outcomes within a population-based single payer healthcare system. Methods: All patients diagnosed with HNC from 2001 to 2010 and referred to any 1 of 5 regional comprehensive cancer centers in British Columbia, Canada were reviewed. Based on census data, patients were classified into 4 categories: 1) rural 2) small urban 3) moderate urban and 4) large urban areas. Kaplan Meier methods and Cox regression were used to correlate site of residence with overall survival (OS), controlling for prognostic factors that included socio-demographics and other tumor and treatment-related characteristics. Results: A total of 3,036 patients were included: median age was 64 years, 74% were men, and 32% were ECOG 0/1. The majority resided in large urban areas (55%) followed by rural (22%), moderate urban (13%), and small urban (10%). There were no clinically significant differences in baseline characteristics across the 4 groups. In multivariate-adjusted models, advanced age >/= 65 years (HR 1.58, 95%CI 1.21-2.06, p<0.001), ECOG 2+ (HR 4.20, 95%CI 2.41-4.93, p<0.001), and lack of multimodality treatment (HR 2.88, 95%CI 1.72-4.81, p<0.001) correlated with inferior OS, but site of residence did not (Table). In subgroup analyses that stratified by type of treatment (radiation, chemotherapy, and/or surgery) and anatomic location of HNC (oral cavity, oropharynx, larynx, hypopharynx, nasopharynx), OS remained similar irrespective of urban or rural residence. Conclusions: Urban-rural differences in outcomes were not observed. The centralization of HNC management in this large population-based cohort represents an appropriate model of care for cancers in which multimodality treatments are increasingly complex and where disparities in access may be prevalent. Residence HR for death 95%CI P-value Rural 1.0 -- -- Small urban 1.27 0.77-2.10 0.35 Moderate urban 0.84 0.52-1.37 0.49 Large urban 1.19 0.80-1.56 0.51
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".