Disparities in the Initial Presentation of Differentiated Thyroid Cancer in a Large Public Hospital and Adjoining University Teaching Hospital
Bibliographic record
Abstract
BACKGROUND: Healthcare disparities associated with insurance and socioeconomic status have been well characterized for several malignancies, such as lung cancer. To assess whether there are healthcare disparities in thyroid cancer, this study evaluated the stage on initial presentation of patients with differentiated thyroid cancer (DTC) in a public versus university teaching hospital. METHODS: A retrospective chart review was performed to identify patients with a new diagnosis of DTC from January 1, 2007, to January 1, 2010, in a large public and adjoining university teaching hospital at a single academic medical center. Medical records were reviewed for demographics, pathology, and American Joint Committee on Cancer tumor-node-metastasis stage at initial presentation. RESULTS: There were 49 cases of well-DTC (96% papillary and 4% Hürthle) in the public hospital and 370 cases (95% papillary, 2% Hürthle, and 3% follicular) in the university teaching hospital. Median age (years) at presentation was 50 in the public versus 48 in the university teaching hospital (p=0.39). Ninety-six percent of public hospital patients were from ethnic minorities compared with 16% of university teaching hospital patients (p<0.0001). Only 1 (2%) public hospital patient had private insurance compared with 85% of university teaching hospital patients. Tumor status (p=0.002) and stage (p=0.03) were more advanced and extrathyroidal extension (p=0.02) was more prevalent among public hospital patients compared with university teaching hospital patients. In a multivariable analysis, public hospital, male gender, increasing age, advanced tumor status, and the presence of lymphovascular invasion were the best predictors of more advanced disease stage. Public hospital patients were 3.4 times more likely to present with advanced DTC than university teaching hospital patients of the same age, gender, tumor status, and lymphovascular invasion status (95% confidence interval 1.29-8.95). CONCLUSIONS: In a public hospital, where the patient population is defined primarily by insurance status, patients were more likely to present with advanced-stage DTC than patients presenting to an adjacent university teaching hospital. These results suggest a disparity in the stage on initial presentation of DTC, possibly resulting in a delayed diagnosis of cancer.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".