Disparities in access to surgical resection in patients with pancreatic cancer – a systematic review
Bibliographic record
Abstract
BACKGROUND: Surgical resection is a crucial component of pancreatic cancer treatment. However, multiple disparities in access to surgical resection have been reported. This systematic review aimed to critically assess and summarize these disparities to improve equity in cancer care. METHODS: PubMed, Web of Science, Embase, Medline, and Cochrane databases were searched from 2000 to 2023. Primary research articles from the United States specifically evaluating surgical resection for resectable pancreatic adenocarcinoma cancer were included. Bias assessment was performed using the modified Newcastle-Ottawa Scale. RESULTS: A total of 19 studies met the final inclusion criteria. 16 studies reported disparities among minority groups, with Black and Hispanic patients less likely to undergo surgery. 15 studies reported older age being predictive of nonreceipt of surgery. Lower socioeconomic status, reported in 8 studies, and nonprivate insurance, reported in 7 studies, were determined to be independent risk factors for decreased receipt of surgery. Five studies reported that patients treated at community hospitals were less likely to receive surgery, and 4 studies identified being single as an independent risk factor for nonreceipt of surgery. Finally, residence in a rural location, reported in 1 study, and male sex, reported in 1 study, were determined to be predictive of decreased receipt of surgery. DISCUSSION: Various sociodemographic factors influence the access to surgical resection for pancreatic cancer. These factors are proxies for multiple underlying barriers along the continuum of care, some of which may be modifiable. Identifying and understanding these barriers will allow us to develop targeted interventions to improve the delivery of oncologic care.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".