Impact of race on survival in patients with clinically nonmetastatic prostate cancer who deferred primary treatment
Bibliographic record
Abstract
BACKGROUND: Prostate cancer (PCa) racial disparity studies typically focus on survival differences after curative treatment. The authors of this report hypothesized that comparing mortality rates between African American (AA) and Caucasian American (CA) patients who deferred primary treatment for clinically nonmetastatic PCa may provide a better assessment of the impact of race on the natural course of PCa. METHODS: The pathology database of the New York Veterans Administration Medical Center (VAMC), an equal access-of-care facility, was searched for patients with biopsy-proven PCa. Inclusion criteria included 1) no evidence of metastatic disease or death within 3 years after diagnosis, 2) no primary treatment, and 3) a minimum of 5 years of follow-up for survivors. RESULTS: In total, 518 patients met inclusion criteria between 1990 and 2005. AA patients were younger (P = .02) and had higher median prostate-specific antigen (PSA) levels (P = .001) at the time of diagnosis compared with CA patients. In a multivariate model, higher Gleason score and PSA level were associated with increased mortality (P = .001 and P = .03, respectively), but race was not a predictor of death from PCa. CONCLUSIONS: The current data suggested that race did not have a major impact on survival in patients with PCa who deferred primary treatment for clinically nonmetastatic disease.
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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".