Pre‐treatment biomarker levels improve the accuracy of post‐prostatectomy nomogram for prediction of biochemical recurrence
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Bibliographic record
Abstract
PURPOSE: We tested the ability of several pre-operative blood-based biomarkers to enhance the accuracy of standard post-operative features for the prediction of biochemical recurrence (BCR) after radical prostatectomy (RP). METHODS: Pre-operative plasma levels of Endoglin, interleukin-6 (IL-6), interleukin-6 soluble receptor (IL-6sR), transforming growth factor-beta1 (TGF-beta1), urokinase plasminogen activator (uPA), urokinase plasminogen inhibitor-1 (PAI-1), urokinase plasminogen receptor (uPAR), vascular cell adhesion molecule-1 (VCAM1), and vascular endothelial growth factor (VEGF) were measured using commercially available enzyme immunoassays in 423 consecutive patients treated with RP for clinically localized prostate cancer. Standard post-operative features consisted of surgical margin status, extracapsular extension, seminal vesicle invasion, lymph node involvement, and pathologic Gleason sum. Multivariable modeling was used to explore the gain in the predictive accuracy. The accuracy was quantified by the c-index statistic and was internally validated with 200 bootstrap resamples. RESULTS: Plasma IL-6 (P = 0.03), IL-6sR (P < 0.001), TGF-beta1 (P = 0.005), and V-CAM1 (P = 0.01) achieved independent predictor status after adjusting for the effects of standard post-operative features. After stepwise backward variable elimination, a model relying on RP Gleason sum, IL-6sR, TGF-beta1, VCAM1, and uPA improved the predictive accuracy of the standard post-operative model by 4% (86.1% vs. 82.1%, P < 0.001). CONCLUSIONS: Pre-operative plasma biomarkers improved the accuracy of established post-operative prognostic factors of BCR by a significant margin. Incorporation of these biomarkers into standard predictive models may allow more accurate identification of patients who are likely to fail RP thereby allowing more efficient delivery of adjuvant therapy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 it