Positive family history as a predictor for disease outcomes after radical prostatectomy for nonmetastatic prostate cancer
Bibliographic record
Abstract
Background While family history (FHx) of prostate cancer (PCa) increases the risk of PCa, comparably less is known regarding the impact of FHx on pathologic and oncologic outcomes after radical prostatectomy (RP).Methods We retrospectively reviewed our multicenter database comprising 6,041 nonmetastatic PCa patients treated with RP. Patients with a FHx of PCa in one or more first-degree relatives were considered as FHx positive. We examined the association of FHx with pathologic outcomes and biochemical recurrence (BCR) using logistic and Cox regression models, respectively.Results In total, 1,677 (28%) patients reported a FHx of PCa. Compared to patients without FHx, those with, were younger at RP (median age of 59 vs. 62 years, p < 0.01), and had significantlymore favorable biopsy and RP histopathologic findings. On multivariable logistic regression analysis, positive FHx was associated with extracapsular extension (odds ratio [OR] 0.77, 95% confidence interval [CI] 0.66–0.90, p < 0.01; model AUC 0.73) and upgrading (OR 0.70, 95% CI 0.62–0.80, p < 0.01; model AUC 0.68). Incorporating FHx significantly improved the AUC of the base model for upgrading (p < 0.01). Positive FHx was not associated with BCR in pre- and postoperative multivariable models (p = 0.1 and p = 0.7); c-indexes of Cox multivariable models were: 0.73 and 0.82, respectively.Conclusions We found that patients with clinically nonmetastatic PCa who have positive FHx of PCa undergo RP at a younger age and have more favorable pathologic outcomes. Nevertheless, FHx of PCa did not confer better BCR rates, suggesting that FHx leads to potentially early detection and treatment without impact on BCR.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".