An Analysis on the Effect of Income Changes in the Resection of Early-Stage Pancreatic Adenocarcinoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it