Survival benefit of local versus no local treatment for metastatic prostate cancer—Impact of baseline PSA and metastatic substages
Bibliographic record
Abstract
BACKGROUND: To test whether local treatment (LT), namely radical prostatectomy (RP) or brachytherapy (BT) still confers a survival benefit versus no local treatment (NLT), when adjusted for baseline PSA (bPSA). To further examine whether the effect of LT might be modulated according to bPSA and M1 substages. METHODS: Of 13 906 mPCa patients within the SEER (2004-2014), 375 underwent RP, 175 BT, and 13 356 NLT. Multivariable competing risks regression (MVA CRR) analyses after 1:2 propensity score matching assessed the impact of LT versus NLT on cancer specific mortality (CSM). Interaction analyses tested the association between treatment type and bPSA within different M1 substages. RESULTS: MVA CRR analyses revealed lower CSM rates for LT (RP [HR: 0.55, CI: 0.44-0.70, P < 0.001] and BT [HR: 0.63, CI: 0.49-0.83, P < 0.001]) compared to NLT. A significant interaction existed between bPSA and treatment type, in M1b patients only. Here, LT conferred a survival benefit when bPSA was <60 ng/mL with maximum benefit when bPSA was <40 ng/mL. No survival benefit existed for M1b patients above the 60 ng/mL bPSA threshold and for M1c patients, regardless of bPSA. For M1a patients, LT conferred a survival benefit compared to NLT. However, dose-response according to bPSA could not be tested, due to insufficient sample size. CONCLUSIONS: Our observations provide new insight regarding the pivotal effect of bPSA and M1 substages on CSM, when LT is contemplated. While M1a patients benefited from LT, the survival benefit was modulated by bPSA in M1b patients and no survival benefit existed in M1c patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".