Assessment of mediators of racial disparities in cervical cancer survival in the <scp>U</scp>nited <scp>S</scp>tates
Bibliographic record
Abstract
Cervical cancer (CC) morbidity and mortality have decreased in the United States, but they remain high among black women. We assessed racial disparities in CC mortality, accounting for socioeconomic status (SES). We linked data from the 1988 to 2007 Surveillance Epidemiology and End Results (SEER) database to the US Census. Additional SES information was obtained through linkage with Area Resource Files. We used the Kaplan-Meier method for estimating probabilities following CC diagnosis and Cox proportional hazards regression to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CC mortality by race. The models were incrementally adjusted for marital status, registry, period, stage, age at diagnosis, histology, treatment, household income, poverty and unemployment rates. We stratified the analyses by disease stage and American state. A total of 44,554 women with CC were identified. Compared to white women, black women had a higher risk of dying from CC; crude and adjusted HRs were 1.41 (CI: 1.34-1.48) and 1.09 (CI: 1.03-1.15), respectively. Corresponding estimates for Hispanic women were 0.85 (CI: 0.80-0.89) and 0.75 (CI: 0.71-0.80). Black women diagnosed at late disease stages had a higher risk of CC death, whereas Hispanic women diagnosed at early and late stages had significantly lower risks. Black CC patients in California experienced poorer survival relative to white women. Conversely, longer CC survival was seen among Hispanic women in California, Georgia and Utah. While crude estimates indicated an increased CC death risk among black women, risks diminished upon adjustment for clinical and sociodemographic characteristics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".