Racial/ethnic disparities in the distribution and effect of type and number of high-risk criteria on mortality in prostate cancer patients treated with radiotherapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: To assess differences in the distribution of type and number of D'Amico high-risk criteria (DHRCs) according to race/ethnicity (R/E) and their effect on cancer-specific mortality (CSM) in prostate cancer (PCa) patients treated with external beam radiotherapy (RT). Methods: In the SEER database (2004-2016), we identified 31,002 PCa patients treated with RT with at least one DHRCs, namely PSA >20 ng/dL, biopsy Gleason Grade Group 4-5, and clinical T stage ≥T2c. Competing risks regression (CRR) model tested the association between DHRCs and 5-year CSM in all R/E subgroups. Results: Of 31,002 patients, 20,894 (67%) were Caucasian, 5256 (17%) were African American, 2868 (9.3%) were Hispanic-Latino, and 1984 (6.4%) were Asian. The distributions of individual DHRCs and combinations of two DHRCs differed according to R/E, but not for the combination of three DHRCs. The effect related to the presence of a single DHRC, and combinations of two or three DHRCs on absolute CSM rates was lowest in Asians (1.2-6.8%), followed by in African Americans (2.3-12.2%) and Caucasians (2.3-12.1%), and highest in Hispanic/Latinos (1.7-13.8%). However, the opposite effect was observed in CRR, where hazard ratios were highest in Asians vs. other R/Es: Asians 1.00-2.59 vs. others 0.5-1.83 for one DHRC, Asians 3.4-4.75 vs. others 0.66-3.66 for two DHRCs, and Asians 7.22 vs. others 3.03-4.99 for all three DHRCs. Conclusions: R/E affects the proportions of DHRCs. Moreover, within the four examined R/E groups, the effect of DHRCs on absolute and relative CSM metrics also differed. Therefore, R/E-specific considerations may be warranted in high-risk PCa patients treated with RT.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 it