IS RACE A PROGNOSTIC FACTOR IN DETERMINING OVARIAN CANCER SURVIVAL OUTCOMES? THE ANSWER IS NOT JUST <i>BLACK AND WHITE</i>
Bibliographic record
Abstract
Objective To determine if there are disparities in survival outcomes in African–American (AA) and white American (WA) women who seek treatment for ovarian cancer in the USA. Background Among gynaecologic malignancies in the USA, epithelial ovarian cancer continues to be the leading cause of death, surpassing uterine and cervical cancer combined. An estimated 21 990 women were diagnosed with ovarian cancer in 2011 in the USA. Despite advances in surgical techniques and chemotherapy that have provided an increasing number of treatment options for women with this disease, mortality remains high, with 15 460 women dying from ovarian cancer in 2011. These outcomes reflect the fact that there is no effective screening tool for ovarian cancer. Thus, the majority of cases are diagnosed in advanced stages, and prognosis is usually poor. In addition, race has been postulated to be a prognostic factor of survival in women with ovarian cancer in a number of studies. Large epidemiological studies have demonstrated a lower incidence and death rate with epithelial ovarian cancer for AA compared with Caucasians. However, relative survival for AA appears to be significantly poorer. This research hypothesizes that this difference is not biologically determined, rather, it is the result of social inequalities. A literature review is required to assess the relationship between race and survival outcomes of ovarian cancer in AA and WA women in the USA. Methods Literature review of English peer-reviewed studies regarding differences in survival outcomes for black and white ovarian cancer patients in the USA. Studies assessed by HRs, forest plots, Kaplan–Meier survival plots, 5-year survival rates and statistical significance. Results Statistically significant results were demonstrated in two studies: Chan et al, 2008 (HR: 1.179, 1.095 to 1.270, 95% CI) Albain et al, 2009 (HR: 1.48, 1.03 to 2.11, 95% CI). 5-year survival rate (AA vs WA): Chan et al, 2008 (40.7% vs 44.1%), Albain et al, 2009 (17.8% vs 29.9%). Conclusions Despite statistically significant evidence demonstrating that AA women experience worse survival outcomes in ovarian cancer than WA women, it would be premature to conclude that race is the only prognostic factor. Race is a social determinant that influences other variables that affect treatment and survival of ovarian cancer, such as education, occupation, socioeconomic status and access to healthcare. Therefore, future studies are required to further assess the complicated relationship between survival outcomes and race.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".