Role of physician density in predicting stage and survival for head and neck squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Identifying and linking barriers to access to head and neck cancer care, specifically provider density, to stage of diagnosis and survival outcomes is important to serve as a foundation for policy interventions. METHODS: Retrospective cohort study using patients with head and neck squamous cell (HNSCC) in the Surveillance, Epidemiology, and End Results (SEER) database from 2007 to 2016 and Area Resource File. Primary outcomes included stage of presentation and cancer-specific 5-year survival and relation to provider density. RESULTS: The initial cohort consisted of 18 342 patients with oral cavity, 21 809 oropharyngeal, 15 860 laryngeal, and 2887 patients with hypopharyngeal malignancy. Non-Hispanic Black race and being uninsured increased the odds of presenting with advanced stage HNSCC and increased hazard of death. There was no significant and consistent association identified between Health Service Areas provider density and advanced stage at diagnosis or cancer-specific 5-year mortality. CONCLUSIONS: Provider density of otolaryngologists and primary care physicians and dentists was not significantly associated with stage of presentation or cancer-specific survival for HNSCC while race and insurance status remained independent predictors for worse outcomes.
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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 | 0.009 |
| 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".