DISP-02. PATTERNS IN THE INCIDENCE OF BRAIN METASTASIS BY RACE AND ETHNICITY: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Abstract INTRODUCTION Despite advancements in cancer treatment, the incidence of brain metastases (BM) continues to rise which necessitates risk factor analyses. Although current literature indicates racial variance in BM incidence, there exists a major knowledge gap in understanding associations between race/ethnicity, BM incidence, and subtypes which emphasizes the need for a systematic review. We aimed to analyze the relationship between BM incidence, race/ethnicity, and primary cancer type. METHODS Using PRISMA guidelines, a systematic review of PubMed and Ovid databases from January 2000 to January 2023 for terms related to BM, ethnicity and race, in conjunction with incidence was conducted. Inclusion criteria comprised peer-reviewed journals with ages > 18, sample size > 100, primary malignancy diagnosis with evidence of BM, and descriptions of patient race or ethnicity. RESULTS Of 806 identified studies, 11 articles primarily utilizing US data were included for final analysis. Lung and bronchus cancers, revealed the highest incidence of BM in Asian patients, followed by Black, White, and American Indian/Alaska Natives (P< .001), despite overall increased incidence of primary cancer in Black patients. Breast cancer revealed highest BM incidence in American Indian/Alaska Natives (5.9%), followed by Black (5.0%), White (3.6%), and Asian/Pacific Islanders (3.5%) (P< .001). When compared to White and Hispanics only, Black patients had greater incidences of BM (OR: 2.26, 95% CI: 1.57–3.25). Lower incidence of BM for esophageal cancer was observed in Black patients compared to White patients (OR=0.38; 95% CI: (0.16–0.89); P= 0.026). No significant differences were observed in BM incidence for bladder, prostate, or non-small cell and small cell lung cancers. CONCLUSIONS Our review suggests that BM incidence does vary amongst racial/ethnic groups dependent on primary cancer type. Differences may be due to numerous factors, underscoring the need for more work with inclusive, diverse cohorts to tackle BM disparities.
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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 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".