The Effect of Insurance Status on Treatment Modality in Advanced Oral Cavity Cancer
Bibliographic record
Abstract
Objectives: Insurance status has been shown to impact survival outcomes. We sought to determine whether insurance affects the choice of treatment modality among patients with advanced (T4) oral cavity squamous cell carcinoma (OCSCC). Design: Retrospective, population-based cohort study Setting: The Survival, Epidemiology, and End Results (SEER) Program database Participants: The population included all adult (age > 18) patients with advanced (T4a or T4b) OCSCC diagnosed from 2007 to 2016. Main outcome measures: The main outcome measure was the odds of receiving definitive treatment, defined as primary surgical resection. Insurance status was categorized into uninsured, any Medicaid, and insured groups. Univariable, multivariable, and subgroup analyses were performed. Results: The study population consisted of 2628 patients, of whom 1915 (72.9%) were insured, 561 (21.3%) had Medicaid, and 152 (5.8%) were uninsured. The multivariable model showed that patients who were 80 years or older, unmarried, with T4b disease, received treatment in the pre-Affordable Care Act (ACA) period, and who were on Medicaid or uninsured were significantly less likely to receive definitive treatment. Among patients with T4a disease, insured patients were significantly more likely to receive definitive treatment compared to those on Medicaid or uninsured (OR=0.69, 95% CI 0.54-0.88, p<0.0001 [Medicaid vs. Insured]; and OR=0.65, 95% CI 0.43-0.98, p=0.001 [Uninsured vs. Insured]), however these differences did not persist when considering only those patients treated following the 2014 expansion of the ACA. Conclusions: Insurance status is significantly associated with treatment modality among adults with advanced stage (T4a) OCSCC. These findings support the premise of expanding insurance coverage in the United States.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".