Adjuvant therapy (AT) following resection of pancreatic ductal adenocarcinoma (PDAC): Are patients from rural, remote areas disadvantaged?
Bibliographic record
Abstract
373 Background: AT with chemotherapy (CT) + radiation (RT) has been shown to improve PDAC survival over surgery alone. Although race and socioeconomic status can affect outcomes in PDAC, the impact of rural or remote residence on the delivery and effect of AT has not been studied. Methods: Patients undergoing pancreatectomy for PDAC were identified from the National Cancer Data Base between 2006 and 2013. Individuals were classified as living in a metro area, urban/rural adjacent to metro area (URA), and urban/rural remote area (URR). Patients with less than 6 months follow-up were excluded. Logistic regression was performed to assess residence as a predictor of receiving AT. Overall survival (OS) as a function of inhabitance was estimated by the method of Kaplan and Meier and prognostic factors were identified by Cox regression. Results: A total of 32,521 individuals underwent pancreatectomy for PDAC. The majority of AT was delivered in academic research facilities in 56% of patients while only 29% of patients received both CT and RT. Univariate analysis demonstrated individuals in URR were less likely to receive CT (55% vs 58%, p < 0.01) but not RT (30% vs 31%, p < 0.261) and had a longer interval to AT (82 vs 75 days, p < 0.009) than those in metro areas. However on multivariate analysis URR inhabitance was no longer predictive of any form of AT (OR = 0.892, 95% CI: 0.792-1.006, p = 0.062). Hispanic ethnicity, Medicaid insurance, uninsured status, and lower education were all predictive of decreased likelihood of receiving AT. Median OS was inferior for URR dwellers with pathologic T2 and T3 tumors compared to those in metro areas (19.8 vs. 24.4 months, p = 0.044 and 17.5 vs. 19.4 months, p < 0.001). Cox regression revealed URR location remained independently associated with poorer OS (HR 1.076, 95% CI: 1.008-1.149, p < 0.029). Conclusions: While living in a URR does not lead to reduced access to AT, it is associated with a worse OS in resected PDAC. This may be due to inadequate AT or other socioeconomic factors present in URR patients. Attention must be focused on improving oncologic care for groups susceptible to treatment 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".