Effect of positive surgical margins at radical prostatectomy on cancer‐specific mortality in high/very high‐risk prostate cancer patients with Gleason Grade Group 4–5
Bibliographic record
Abstract
BACKGROUND: The effect of positive surgical margins (PSM) on cancer specific mortality (CSM) in high/very high-risk (HR/VHR) prostate cancer (PCa) with aggressive Gleason Grade Group (GGG) is unknown. We tested PSM effect on CSM in this setting, in addition to testing of radiotherapy (RT) benefit in PSM patients. METHODS: We relied on Surveillance, Epidemiology, and End Results database (2010-2015), focusing on HR/VHR patients with exclusive GGG 4-5 at radical prostatectomy (RP). Kaplan-Meier plots and multivariable Cox regression models tested the relationship between PSM and CSM. Moreover, the effect of RT on CSM was explored in PSM patients. RESULTS: Of 3383 HR/VHR patients, 15.1% (n = 511) exhibited PSM. Patients with PSM harbored higher rates of GGG 5 (60.1% vs. 50.9%, p < 0.001), pathologic tumor stage T3a (69.1% vs. 45.2%, p < 0.001) and lymph node involvement (14.1% vs. 9.4%, p < 0.001), relative to patients without PSM. PSM rates decreased over time (2010-2015) from 16.0% to 13.6%. Seven-year CSM-free survival rates were 91.6% versus 95.7% in patients with and without PSM, respectively. In multivariable Cox regression models, PSM was an independent predictor of CSM (hazard ratio = 1.6, p = 0.040) even after adjustment for age, prostate specific antigen, pathologic tumor stage and lymph node status. Finally, in PSM patients, RT delivery did not reduce CSM in either univariable or multivariable Cox regression models. CONCLUSIONS: In HR/VHR PCa patients with exclusive GGG 4-5, PSM at RP adversely affect survival. Moreover, RT has no protective effect on CSM. In consequence, lowest possible PSM rates are crucial in such 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".