An Analysis on the Effect of Income Changes in the Resection of Early-Stage Pancreatic Adenocarcinoma
Bibliographic record
Abstract
INTRODUCTION: The impact of socioeconomic inequalities on cancer care and outcomes has been well recognized and the underlying causes are likely multifactorial. Income is regarded as a cornerstone of socioeconomic status and has been assumed to correlate with access to care. We therefore sought to investigate whether income and changes in income would affect the rate of patients undergoing surgical resection for early-stage pancreatic cancer. METHODS: Inflation-adjusted income data were obtained from the United States Census Bureau from 2010 to 2019. The cancer data were obtained from the SEER database. Counties present in both data sets were included in the analysis. Patients with stage I or II pancreatic cancer who underwent formal resection were deemed to have undergone appropriate surgical management. Patients were grouped into an early (2010-2014) and late (2015-2019) time period. RESULTS: < .001). The median change in income between the two time periods was an increase by $2387. The rate of resection was not dependent on income class or income change in our study population. CONCLUSION: Our surgical care of pancreatic cancer is improving with more patients undergoing resection. In addition, there are now fewer disparities between patients of lower-income and higher-income groups with respect to receiving surgical intervention. This implies that our access to care has improved over the past decade. This is an encouraging finding with regards to reducing health care disparities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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) | 0.003 | 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".