Initiation of adjuvant therapy following surgical resection of pancreatic ductal adenocarcinoma (PDAC): Are patients from rural, remote areas disadvantaged?
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although race and socioeconomic status have been shown to affect outcomes in pancreatic ductal adenocarcinoma (PDAC), the impact of rural residence on the delivery of adjuvant therapy (AT) has not been studied. METHODS: Patients with resected PDAC were identified using the National Cancer Database (NCDB). Individuals were classified as living in a metro area, urban/rural adjacent to a metro area (URA), and urban/rural remote (URR) area. Multivariate logistic regression was used to assess geographic inhabitance as a predictor of receiving AT. RESULTS: A total of 32 521 individuals who underwent pancreatectomy for PDAC were identified. Univariate analysis demonstrated individuals in URR areas were less likely to receive adjuvant chemotherapy (ACT) than those living in URA or metro areas (55.3% vs 55.6% vs 58.8%, P = 0.011). However on multivariate analysis URR inhabitance was no longer a predictor of ACT (OR = 0.911 P = 0.125) or ART (OR = 0.953 P = 0.462). Cox proportional hazard modeling demonstrated URR inhabitance remained independently associated with poor OS (HR 1.076; 95% CI [1.008, 1.149], P < 0.029). CONCLUSIONS: URR inhabitance does not impact access to AT, however it is independently associated with a decreased OS. Attention must be focused on optimizing oncologic care to patients with disparate access to healthcare.
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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 | 0.001 | 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".