Trends and Predictors of Palliative Therapy Use in Young Adults with Advanced Gastrointestinal Cancer: A National Cancer Database Study
Bibliographic record
Abstract
BACKGROUND: Young adults (YAs) with advanced gastrointestinal (GI) cancer have unique care needs, which may be addressed through palliative therapy. OBJECTIVES: The aims of this study were to describe temporal trends and identify predictors of palliative therapy utilization in YAs with advanced GI cancer. METHODS: We conducted a retrospective cohort study using the National Cancer Database. YAs (18-39 years of age) diagnosed with advanced GI cancer from 2004 to 2020 were identified. We performed a trend analysis followed by univariable and multivariable logistic regression analyses. RESULTS: < 0.05). Patients of non-White/non-Black race (odds ratio [OR] 1.23, 95% confidence interval [CI] 1.09-1.40), with no insurance (OR 1.35, 95% CI 1.20-1.53), and with a median income of less than $63,000 (OR 1.20, 95% CI 1.08-1.34) were more likely to receive palliative therapy. Multiple comorbidities (OR 1.59, 95% CI 1.24-2.06), stage IV disease (OR 8.28, 95% CI 7.33-9.34), and cancers of the esophagus (OR 2.26, 95% CI 1.88-2.71), liver (OR 2.19, 95% CI 1.88-2.56), pancreas (OR 2.20, 95% CI 1.53-3.16), and biliary tract (OR 2.12, 95% CI 1.54-2.91) were also predictors of palliative therapy utilization. CONCLUSIONS: Palliative therapy utilization in YAs with advanced GI cancer increased significantly over the study period, however major gaps remain in the provision of this care. Further work is needed to understand the barriers to access among YAs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".