Prostate Cancer Grade and Stage Misclassification in Active Surveillance Candidates: Black Versus White Patients
Bibliographic record
Abstract
BACKGROUND: Misclassification rates defined as upgrading, upstaging, and upgrading and/or upstaging have not been tested in contemporary Black patients relative to White patients who fulfilled criteria for very-low-risk, low-risk, or favorable intermediate-risk prostate cancer. This study aimed to address this void. METHODS: Within the SEER database (2010-2015), we focused on patients with very low, low, and favorable intermediate risk for prostate cancer who underwent radical prostatectomy and had available stage and grade information. Descriptive analyses, temporal trend analyses, and multivariate logistic regression analyses were used. RESULTS: Overall, 4,704 patients with very low risk (701 Black vs 4,003 White), 17,785 with low risk (2,696 Black vs 15,089 White), and 11,040 with favorable intermediate risk (1,693 Black vs 9,347 White) were identified. Rates of upgrading and/or upstaging in Black versus White patients were respectively 42.1% versus 37.7% (absolute Δ = +4.4%; P<.001) in those with very low risk, 48.6% versus 46.0% (absolute Δ = +2.6%; P<.001) in those with low risk, and 33.8% versus 35.3% (absolute Δ = -1.5%; P=.05) in those with favorable intermediate risk. CONCLUSIONS: Rates of misclassification were particularly elevated in patients with very low risk and low risk, regardless of race, and ranged from 33.8% to 48.6%. Recalibration of very-low-, low-, and, to a lesser extent, favorable intermediate-risk active surveillance criteria may be required. Finally, our data indicate that Black patients may be given the same consideration as White patients when active surveillance is an option. However, further validations should ideally follow.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".