The Impact of Racial Disparities on Outcome in Patients With Stage IIIC Endometrial Carcinoma
Bibliographic record
Abstract
OBJECTIVE: To report the impact of race on clinical outcomes in patients with stage IIIC endometrial carcinoma. MATERIALS AND METHODS: A retrospective multi-institutional study included 90 black and 568 non-black patients with stage IIIC endometrial carcinoma who received adjuvant chemotherapy and radiation treatments. Overall survival (OS) and recurrence-free survival (RFS) were calculated by the Kaplan-Meier method. Propensity score matching (PSM) was conducted. Statistical analyses were conducted using SPSS version 27. RESULTS: The Median follow-up was 45.3 months. black patients were significantly older, had more nonendometrioid histology, grade 3 tumors, and were more likely to have >1 positive paraaortic lymph nodes compared with non-black patients (all P <0.0001). The 5-year estimated OS and RFS rates were 45% and 47% compared with 77% and 68% for black patients versus non-black patients, respectively ( P <0.001). After PSM, the 2 groups were well-balanced for all prognostic covariates. The estimated hazard ratios of black versus non-black patients were 1.613 ( P value=0.045) for OS and 1.487 ( P value=0.116) for RFS. After PSM, black patients were more likely to receive the "Sandwich" approach and concurrent chemoradiotherapy compared with non-black ( P =0.013) patients. CONCLUSIONS: Black patients have higher rates of nonendometrioid histology, grade 3 tumors, and number of involved paraaortic lymph nodes, worse OS, and RFS, and were more likely to receive the "Sandwich" approach compared with non-black patients. After PSM, black patients had worse OS with a nonsignificant trend in RFS. Access to care, equitable inclusion on randomized trials, and identification of genomic differences are warranted to help mitigate 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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".