Racial and Ethnic Disparities in Survival among Patients with Cholangiocarcinoma in the United States: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cholangiocarcinoma (CCA) is an increasing cause of mortality in the United States; however, the burden of CCA disproportionately affects racial and ethnic minority groups. We aimed to characterize racial and ethnic differences in stage, treatment, and survival among patients with CCA. METHODS: We systematically searched MEDLINE and Embase through March 2023 for all studies reporting clinical outcomes among patients with CCA stratified by race and ethnicity. We calculated pooled HRs using the DerSimonian and Laird method for a random-effects model. RESULTS: Of 292 articles, 16 met inclusion criteria (n = 248,109 patients). Among six studies (n = 87,938) reporting overall survival, Black patients had worse survival [pooled HR, 1.05; 95% confidence interval (CI), 1.01-1.10], whereas Hispanic (pooled HR, 0.86; 95% CI, 0.83-0.89) and Asian/Pacific Islander (pooled HR, 0.88; 95% CI, 0.85-0.90) patients had better survival than White patients. Compared with White patients, Black and Hispanic patients were less likely to present at an early stage, and Black patients were less likely to undergo resection (pooled OR, 0.69; 95% CI, 0.63-0.75). The limitations of studies were lack of granularity on subtype and risk of residual confounding. CONCLUSIONS: There are racial and ethnic differences in CCA prognosis in the United States, with Black patients having worse survival and Hispanic and Asian patients having better survival than White patients. Studies are needed to identify actionable factors underlying this disparity to promote health equity and improve outcomes for patients. IMPACT: There exist racial discrepancies in survival and treatment for CCA; more studies are needed to better understand the extent and causes of discrepancies.
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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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.028 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".