The impact of race on survival in metastatic prostate cancer: a systematic literature review
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PC) is the second most diagnosed cancer in men worldwide. While racial and ethnic differences exist in incidence and mortality, increasing data suggest outcomes by race among men with newly diagnosed PC are similar. However, outcomes among races beyond Black/White have been poorly studied. Moreover, whether outcomes differ by race among men who all have metastatic PC (mPC) is unclear. This systematic literature review (SLR) provides a comprehensive synthesis of current evidence relating race to survival in mPC. METHODS: An SLR was conducted and reported in accordance with PRISMA guidelines. MEDLINE®, Embase, and Cochrane Library using the Ovid® interface were searched for real-world studies published from January 2012 to July 2022 investigating the impact of race on overall survival (OS) and prostate cancer-specific mortality (PCSM) in patients with mPC. A supplemental search of key congresses was also conducted. Studies were appraised for risk of bias. RESULTS: Of 3228 unique records identified, 62 records (47 full-text and 15 conference abstracts), corresponding to 54 unique studies (51 United States and 3 ex-United States) reporting on race and survival were included. While most studies showed no difference between Black vs White patients for OS (n = 21/27) or PCSM (n = 8/9), most showed that Black patients demonstrated improved OS on certain mPC treatments (n = 7/10). Most studies found no survival difference between White patients and Hispanic (OS: n = 6/8; PCSM: n = 5/6) or American Indian/Alaskan Native (AI/AN) (OS: n = 2/3; PCSM: n = 5/5). Most studies found Asian patients had improved OS (n = 3/4) and PCSM (n = 6/6) vs White patients. CONCLUSIONS: Most studies found Black, Hispanic, and AI/AN patients with mPC had similar survival as White patients, while Black patients on certain therapies and Asian patients showed improved survival. Future studies are needed to understand what aspects of race including social determinants of health are driving these findings.
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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 | 0.008 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".