Use of Preoperative Plasma Endoglin for Prediction of Lymph Node Metastasis in Patients with Clinically Localized Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Current predictive tools and imaging modalities are not accurate enough to preoperatively diagnose lymph node metastases in patients with prostate cancer. The aim of the study was to evaluate whether preoperative plasma endoglin improves the prediction of lymph node metastases in patients with clinically localized prostate cancer. EXPERIMENTAL DESIGN: Endoglin levels were measured using a commercially available ELISA assay in banked plasma from 425 patients treated with radical prostatectomy and bilateral lymphadenectomy for clinically localized prostatic adenocarcinoma at two university hospitals between July 1994 and November 1997. Logistic regression analyses were undertaken to evaluate whether endoglin improves the accuracy of a standard preoperative model for prediction of lymph node metastasis and to build a predictive nomogram. RESULTS: Preoperative plasma endoglin levels were higher in patients with higher preoperative total serum prostate-specific antigen (PSA; Spearman correlation coefficient 0.296, P < 0.001), positive surgical margins (P = 0.03), higher pathologic Gleason sum (P = 0.04), and lymph node metastasis (P < 0.001). In a preoperative multivariable logistic regression analysis that included PSA and clinical stage, only preoperative endoglin (odds ratio, 1.17; 95% confidence interval, 1.09-1.26; P < 0.001) and biopsy Gleason sum (odds ratio, 18.57; 95% confidence interval, 1.08-318.36; P = 0.04) were associated with metastasis to lymph nodes. The addition of endoglin to a standard preoperative model (including PSA, clinical stage, and biopsy Gleason sum) significantly improved its accuracy for prediction of lymph node metastasis from 89.4% to 97.8% (P < 0.001). CONCLUSIONS: Preoperative plasma endoglin improves the accuracy for prediction of pelvic lymph node metastasis in patients treated with radical prostatectomy for clinically localized prostate cancer by a statistically and clinically significant margin.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".