A systematic review and meta-analysis of germline BRCA mutations in pancreatic cancer patients identifies global and racial disparities in access to genetic testing
Bibliographic record
Abstract
BACKGROUND: Germline BRCA1 and BRCA2 mutations (gBRCAm) can inform pancreatic cancer (PC) risk and treatment but most of the available information is derived from white patients. The ethnic and geographic variability of gBRCAm prevalence and of germline BRCA (gBRCA) testing uptake in PC globally is largely unknown. MATERIALS AND METHODS: We carried out a systematic review and prevalence meta-analysis of gBRCA testing and gBRCAm prevalence in PC patients stratified by ethnicity. The main outcome was the distribution of gBRCA testing uptake across diverse populations worldwide. Secondary outcomes included: geographic distribution of gBRCA testing uptake, temporal analysis of gBRCA testing uptake in ethnic groups, and pooled proportion of gBRCAm stratified by ethnicity. The study is listed under PROSPERO registration number #CRD42022311769. RESULTS: A total of 51 studies with 16 621 patients were included. Twelve of the studies (23.5%) enrolled white patients only, 10 Asians only (19.6%), and 29 (56.9%) included mixed populations. The pooled prevalence of white, Asian, African American, and Hispanic patients tested per study was 88.7%, 34.8%, 3.6%, and 5.2%, respectively. The majority of included studies were from high-income countries (HICs) (64; 91.2%). Temporal analysis showed a significant increase only in white and Asians patients tested from 2000 to present (P < 0.001). The pooled prevalence of gBRCAm was: 3.3% in white, 1.7% in Asian, and negligible (<0.3%) in African American and Hispanic patients. CONCLUSIONS: Data on gBRCA testing and gBRCAm in PC derive mostly from white patients and from HICs. This limits the interpretation of gBRCAm for treating PC across diverse populations and implies substantial global and racial disparities in access to BRCA testing in PC.
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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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".