Influence of Socioeconomic Status on Stage at Presentation of Laryngeal Cancer in the United States
Bibliographic record
Abstract
Objective Identify socioeconomic predictors of stage at diagnosis of laryngeal cancer in the United States. Study Design Retrospective analysis of the North American Association of Central Cancer Registries’ Incidence Data–Cancers in North America Deluxe Analytic File for expanded races. Setting All centers reporting to the US Centers for Disease Control and Prevention’s National Program of Cancer Registries. Subjects and Methods All cases of laryngeal cancer in adult patients from 2005 to 2013 were reviewed. Ordinal logistic regression models were used to evaluate odd ratios (ORs) for socioeconomic indicators potentially predictive of advancing American Joint Committee on Cancer stage at diagnosis. Results A total of 72,472 patients were identified and included. Analysis revealed significant correlation between advanced stage at diagnosis and: Medicaid insurance, lack of insurance, female sex, older age, black race, and certain states of residence. The strongest predictor of advanced stage was lack of insurance (OR, 2.212; P <. 001; 95% CI, 2.035‐2.406). The strongest protective factor was residing in the state of Utah (OR, 0.571; P <. 001; 95% CI, 0.536‐0.609). Once adjusted for regional price and wage disparities, relative income was not a significant predictor of stage at presentation across multiple analyses. Conclusion Multiple socioeconomic factors were predictive of severity of disease at presentation of laryngeal cancer in the United States. This study demonstrated that insurance type was strongly predictive, whereas relative income had surprisingly little influence.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".