Time Kills: Impact of Socioeconomic Deprivation on Timely Access to Guideline-Concordant Treatment in Foregut Cancer
Bibliographic record
Abstract
BACKGROUND: Receipt of guideline-concordant treatment (GCT) is associated with improved prognosis in foregut cancers. Studies show that patients living in areas of high neighborhood deprivation have worse healthcare outcomes; however, its effect on GCT in foregut cancers has not been evaluated. We studied the impact of the area deprivation index (ADI) as a barrier to GCT. STUDY DESIGN: A single-institution retrospective review of 498 foregut cancer patients (gastric, pancreatic, and hepatobiliary adenocarcinoma) from 2018 to 2022 was performed. GCT was defined based on National Comprehensive Cancer Network guidelines. ADI, a validated measure of neighborhood disadvantage was divided into terciles (low, medium, and high) with high ADI indicating the most disadvantage. RESULTS: Of 498 patients, 328 (66%) received GCT: 66%, 72%, and 59% in pancreatic, gastric, and hepatobiliary cancers, respectively. Median (interquartile range) time from symptoms to workup was 6 (3 to 13) weeks, from diagnosis to oncology appointment was 4 (1 to 10) weeks, and from oncology appointment to treatment was 4 (2 to 10) weeks. Forty-six percent were diagnosed in the emergency department. On multivariable analyses, age 75 years or older (odds ratio [OR] 0.39 [95% CI 0.18 to 0.87]), Black race (OR 0.52 [95% CI 0.31 to 0.86]), high ADI (OR 0.25 (95% CI 0.14 to 0.48]), 6 weeks or more from symptoms to workup (OR 0.44 [95% CI 0.27 to 0.73]), 4 weeks or more from diagnosis to oncology appointment (OR 0.76 [95% CI 0.46 to 0.93]), and 4 weeks or more from oncology appointment to treatment (OR 0.63 [95% CI 0.36 to 0.98]) were independently associated with nonreceipt of GCT. CONCLUSIONS: Residence in an area of high deprivation predicts nonreceipt of GCT. This is due to multiple individual- and system-level barriers. Identifying these barriers and developing effective interventions, including community outreach and collaboration, leveraging telehealth, and increasing oncologic expertise in underserved areas, may improve access to GCT.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".