Socioeconomic factors in head and neck cancer.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of socioeconomic status on the incidence of head and neck cancer using a population-based survey. METHODS: We employed pooled individual-level data from the US National Institutes of Health Survey for the years 1997 to 2006 inclusive. We performed a logistic regression analysis for four variables of socioeconomic status (marital status, family income, highest level of education achieved, immigration status) and four potential confounding variables (age, race, smoking status, alcohol consumption). The effects of these socioeconomic variables on head and neck cancer were compared with their effects on cancers overall. RESULTS: There was a statistically significant increase in head and neck cancer incidence for adult men with the following characteristics: status as single, never married and education less than high school completion. There was a trend toward higher rates of head and neck cancer with annual family income < $20,000 US. No such associations were seen for cancer in general. CONCLUSIONS: There is evidence to support the contention that individuals with more disadvantaged socioeconomic status have higher rates of developing head and neck cancer, even after controlling for associated health behaviours such as smoking and alcohol consumption. This work suggests that further study into the effects of socioeconomic deprivation and head and neck cancer is warranted.
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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 | 0.003 |
| 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 | 0.000 |
| 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".