The association of the type and number of D’Amico high-risk criteria with rates of pathologically non-organ-confined prostate cancer
Bibliographic record
Abstract
Introduction: The aim of this study was to assess the association between the type and number of D'Amico high-risk criteria (DHRCs) with rates of pathologically non-organ-confined (NOC) prostate cancer in patients treated with radical prostatectomy (RP) and pelvic lymphadenectomy (PLND). Material and methods: In the Surveillance, Epidemiology, and End Results database (2004-2016), we identified 12961 RP and PLDN patients with at least one DHRC. We relied on descriptive statistics and multivariable logistic regression models. Results: Of 12 961 patients, 6135 (47%) exclusively harboured biopsy Gleason score (GS) 8-10, 3526 (27%) had clinical stage ≥T2c, and 1234 (9.5%) had prostate-specific antigen (PSA) >20 ng/mL. Only 1886 (15%) harboured any combination of 2 DHRCs. Finally, all 3 DHRCs were present in 180 (1.4%) patients. NOC rates increased from 32% for clinical T stage ≥T2c to 49% for either GS 8-10 only or PSA >20 ng/mL only and to 66-68% for any combination of 2 DHRCs, and to 84% for respectively all 3 DHRCs, which resulted in a multivariable logistic regression OR of 1.00, 2.01 (95% CI 1.85-2.19; p <0.001), 4.16 (95% CI 3.69-4.68; p <0.001), and 10.83 (95% CI 7.35-16.52; p <0.001), respectively. Conclusions: Our study indicates a stimulus-response effect according to the type and number of DHRCs. Hence, a formal risk-stratification within high-risk prostate cancer patients should be considered in clinical decision-making.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".