Survival of patients with head and neck squamous cell carcinoma by housing subsidy in a tiered public housing system
Bibliographic record
Abstract
BACKGROUND: Socioeconomic status affects survival in patients diagnosed with head and neck squamous cell carcinoma (HNSCC), even in health systems with universal health care. Singapore has a tiered subsidized housing system, in which income determines eligibility for subsidies by size of apartment. The objective of this study was to assess whether a patient's residential type (small/heavily subsidized, medium/moderate subsidy, large/minimal or no subsidy) influenced mortality. A secondary analysis examined whether patients in smaller subsidized apartments were more likely to present with advanced disease. METHODS: An historical cohort study of patients in a tertiary referral center with HNSCC was identified in the multidisciplinary cancer database from 1992 to 2014. Clinicopathologic data were extracted for analysis. Patient residential postal codes were matched to type of housing. Logistic regression was performed to evaluate the relationship between all-cause mortality and the predictors of interest as well as the association between housing type and disease stage at presentation. RESULTS: Of the 758 patients identified, most were men (73.4%), the median age was 64 years, 30.5% and 15.2% were smokers and former smokers, respectively. Over one-half (56.8%) of patients presented with advanced disease. Male gender, age, stage at presentation, survival time from diagnosis, and smoker status were significant predictors of mortality. Patients living in the smaller, higher subsidy apartments had poorer survival, although they were not more likely to present with advanced disease, suggesting that the survival difference was not because of delayed presentation. CONCLUSIONS: Patients with HNSCC living in smaller, higher-subsidy apartments have poorer survival despite no apparent delays in presentation. Cancer 2017;123:1998-2005. © 2017 American Cancer Society.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".