Effect of chemotherapy in metastatic prostate cancer according to race/ethnicity groups
Bibliographic record
Abstract
BACKGROUND: No North-American study tested the survival benefit of chemotherapy in de novo metastatic prostate cancer according to race/ethnicity. We addressed this void. METHODS: We identified de novo metastatic prostate cancer patients within the Surveillance, Epidemiology, and End Results database (2014-2015). Separate and specific Kaplan-Meier plots and Cox regression models tested for overall survival differences between chemotherapy-exposed versus chemotherapy-naïve patients in four race/ethnicity groups: Caucasian versus African-American versus Hispanic/Latino vs Asian. Race/ethnicity specific propensity score matching was applied. Here, additional landmark analysis was performed. RESULTS: Of 4232 de novo metastatic prostate cancer patients, 2690 (63.3%) were Caucasian versus 783 (18.5%) African-American versus 504 (11.8%) Hispanic/Latino versus 257 (6.1%) Asian. Chemotherapy rates were: 21.3% versus 20.8% versus 21.0% versus 20.2% for Caucasians versus African-Americans versus Hispanic/Latinos versus Asians, respectively. At 30 months of follow-up, overall survival rates between chemotherapy-exposed versus chemotherapy-naïve patients were 61.5 versus 53.2% (multivariable hazard ratio [mHR]: 0.76, 95 confidence interval [CI]: 0.63-0.92, p = 0.004) in Caucasians, 55.2 versus 51.6% (mHR: 0.76, 95 CI: 0.54-1.07, p = 0.11) in African-Americans, 62.8 versus 57.0% (mHR: 1.11, 95 CI: 0.73-1.71, p = 0.61) in Hispanic/Latinos and 77.7 versus 65.0% (mHR: 0.31, 95 CI: 0.11-0.89, p = 0.03) in Asians. Virtually the same findings were recorded after propensity score matching within each race/ethnicity group. CONCLUSIONS: Caucasian and Asian de novo metastatic prostate cancer patients exhibit the greatest overall survival benefit from chemotherapy exposure. Conversely, no overall survival benefit from chemotherapy exposure could be identified in either African-Americans or Hispanic/Latinos. Further studies are clearly needed to address these race/ethnicity specific 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".