The association of type and number of high‐risk criteria with cancer specific mortality in prostate cancer patients treated with radiotherapy
Bibliographic record
Abstract
BACKGROUND: To assess the association between of type and number of D'Amico high-risk criteria (DHRCs) with rates of cancer-specific mortality (CSM) in prostate cancer (PCa) patients treated with external beam radiotherapy (RT). METHODS: In the Surveillance, Epidemiology, and End Results database (2004-2016), we identified 34,908 RT patients with at least one DHRCs, namely prostate-specific antigen (PSA) >20 ng/dL (hrPSA), biopsy Grade Group (hrGG) 4-5, clinical T stage (hrcT) ≥T2c. Multivariable Cox regression models (CRM), as well as competing risks regression (CRR) model, which further adjust for other cause mortality, tested the association between DHRCs and 5-year CSM. RESULTS: Of 34,908 patients, 14,777 (42%) exclusively harbored hrGG, 5641 (16%) hrPSA, 4390 (13%) had hrcT. Only 8238 (23.7%) harbored any combination of two DHRCs and 1862 (5.3%) had all three DHRCs. Five-year CSM rates ranged from 2.4% to 5.0% when any individual DHRC was present (hrcT, hrPSA, hrGG, in that order), versus 5.2% to 10.5% when two DHRCs were present (hrPSA+hrcT, hrcT+hrGG, hrPSA+hrGG, in that order) versus 14.4% when all three DHRCs were identified. In multivariable CRM hazard ratios relative to hrcT ranged from 1.07 to 1.76 for one DHRC, 2.20 to 3.83 for combinations of two DHRCs, and 5.11 for all three DHRCs. Multivariable CRR yielded to virtually the same results. CONCLUSIONS: Our study indicates a stimulus-response effect according to the type and number of DHRCs. This indicates potential for risk-stratification within HR PCa patients that could be applied in clinical decision making to increase or reduce treatment intensity.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 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".