Impact of socioeconomic status on presentation, treatment and outcomes of patients with pancreatic cancer
Bibliographic record
Abstract
Objective: To assess the impact of socioeconomic status (SES) on the patterns of care and outcomes of patients with pancreatic cancer. Materials & methods: Surveillance, Epidemiology and End Results specialized SES registry has been accessed and patients with pancreatic cancer diagnosed (2000–2015) were evaluated. The following SES variables were included: employment percentage, percent of people above the poverty line, percent of people identified as working-class, educational level, median rent, median household value and median household income. Within this SES registry, patients were classified according to their census-tract SES into three groups (where group-1 represents the lowest SES category and group-3 represents the highest SES category). Multivariable logistic regression analysis was used to assess the impact of SES on access to surgical resection and multivariable Cox regression analysis was used to assess the impact of SES on pancreatic cancer-specific survival. Kaplan–Meier survival estimates were also used to compare overall survival (OS) outcomes according to SES. Results: A total of 83,902 pancreatic cancer patients were included in the current analysis. Within multivariable logistic regression analysis among patients with a localized/regional disease, patients with lower SES were less likely to undergo surgical resection for pancreatic cancer (odds ratio: 0.719; 95% CI: 0.673–0.767; p < 0.001). Among patients with a localized/regional disease who underwent surgical resection, patients with higher SES have better OS (median OS for group-3: 20.0 vs 17.0 months for group-1; p < 0.001). Moreover, patients with lower SES have worse pancreatic cancer-specific survival compared with patients with higher SES: (hazard ratio for group-1 vs group-3: 1.212; 95% CI: 1.135–1.295; p < 0.001). Conclusion: Poor neighborhood SES is associated with more advanced disease at presentation, less probability of surgical resection and even poorer outcomes after surgical resection.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".