Racial/ethnic disparities in breast and gynecologic cancer treatment and outcomes
Bibliographic record
Abstract
PURPOSE OF REVIEW: To review recent research in racial/ethnic disparities in breast and gynecologic cancers, focusing on disparities occurring postdiagnosis. RECENT FINDINGS: Mortality statistics show that of the cancers under study, breast cancer has the greatest impact, and of racial/ethnic groups, African Americans suffer the greatest disparities, with highest mortality rates for breast, uterine and cervical cancers, and second highest for ovarian cancer. Recent studies demonstrated that black breast cancer patients suffer more underuse of appropriate adjuvant therapy, and greater delays in diagnosis and institution of treatments, and blacks and Hispanics suffered greater postsurgical pain and symptomatology. Data indicate that the biology of some breast cancers in blacks is unique and more aggressive. One study demonstrated that more black breast cancer patients died of nonbreast cancer causes and that excessive comorbidity in blacks explained substantial amounts of survival disparity. Research is beginning to identify important disparities in nonblack minority racial/ethnic groups, including Hispanics and South Asian Americans. SUMMARY: Research is continuing to identify and explain an important group of disparities - African American disparities in breast cancer outcomes. Disparities in other minority racial/ethnic groups, and in ovarian, uterine and cervical cancers, are at an emerging stage. Continuing efforts at all fronts are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".