PSA density is superior than PSA and Gleason score for adverse
Bibliographic record
Abstract
Introduction: Prostate-specific antigen (PSA) and its kinetics have changed prostate cancer screening and diagnosis. The aim of the present study was to evaluate their value in prostate cancer prognosis by determining the predictive potential of PSA density for adverse pathologic features after radical prostatectomy, in terms of positive surgical margins (PSM), extracapsular disease (ECD), seminal vesicle invasion (SVI) and/or lymph node invasion (LNI), and to compare their predictive ability with preoperative PSA and biopsy Gleason score.Methods: We retrospectively analysed 285 patients diagnosed with prostate cancer and underwent a retropubic radical prostatectomy for clinically localized disease. Data concerning preoperative PSA, biopsy Gleason score and PSA density were collected and analyzed. PSA density was calculated by dividing preoperative PSA and the pathological volume of the prostate.Results: There was a significant difference in PSA density valuesbetween patients with PSM, ECD, SVI and LNI. Areas under thecurve for PSA density were higher than those of PSA and Gleason score for all parameters of adverse pathology. In multivariate analyses, it was shown that PSA density and Gleason score were the only statistically significant predictors for PSM and ECD, PSA density and PSA for SVI and only PSA density for LNI.Conclusion: PSA density is an accurate predictor for adverse pathology prediction in patients undergoing radical prostatectomy. Theseresults demonstrate that this parameter is useful to determine the aggressiveness of prostate cancer and can be used as an adjunct in predicting outcomes after surgery.
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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 | 0.000 | 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) | 0.001 | 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 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".