Prostate Cancer Lesions by Zone and Race: Does Multiparametric MRI Demonstrate Racial Difference in Prostate Cancer Lesions for African American Men?
Bibliographic record
Abstract
African American (AA) men have increased risk of prostate cancer diagnosis and mortality, but the cause remains unknown. MRI fusion improves diagnosis of localized prostate cancer, particularly in anterior lesions; however, cost and access are limited in a community practice setting. By utilizing a diverse cohort of veterans with equal access to care in a single payer system, we describe prostate cancer detection. We queried a prospectively maintained institutional review board-approved database of men undergoing prostate biopsy for untreated prostate cancer. We included all consecutive patients from October 2017 to February 2020. Statistical analysis including Kaplan–Meier Curves, Fisher’s exact test, and Forest plot was performed. From 246 consecutive patients, 166 were AA and 80 were non-AA. There were similar distributions of PSA, PSAD, and number of targetable lesions between the AA and non-AA cohort (p > 0.05 for all). We found no difference in location on MRI between race groups. There was similar cancer detection, focusing on anterior lesions and rate of positive Gleason grade (≥GG1) and clinically significant (≥GG2) cancer between cohorts. In a predominant AA cohort of veterans, we found similar distribution of location for MRI-targeted lesions, along with rates of tumor detection and aggressiveness of disease. In this single payer veteran population, we did not identify specific biologic differences inherent to tumor detection between AA and non-AA patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".