Comparison of Prostate Specific Antigen and Prostate Specific Antigen Density for Predicting the Degree of Gleason Score of Prostate Cancer
Bibliographic record
Abstract
Introduction: In this study we evaluate the relationship of PSA and PSAD with the degree of Gleason score of prostate cancer in transrectal ultrasound guided biopsy specimens. Methods: From March 2003 to October 2009, 1025 transractal ultrasound guided biopsies were performed in our hospital. PSA was measured by monoclonal antibody method and PSAD was calculated. The Gleason grade of the detected tumors in the biopsy specimens was classified as low, moderate and high grade. Data were analyzed by SPSS software. Results: 292 patients were diagnosed to have prostate adenocarcinoma. There was an acceptable correlation between PSA (P=0.001) and PSAD of the specimens (P = 0.013) with Gleason grades. PSA level showed a statistically significant difference between the low and high grade groups (P=0.005) and the intermediate and high grade groups (p=0.014). A statistically significant difference of PSAD level was seen only between the low and high grade (P=0.006) groups Conclusions: PSA and PSAD are both effective diagnostic tools for detection of prostate cancer; PSA level has a valuable role in predicting Gleason pattern higher than 7/10 and it can be the predictor of advanced pathological features but PSAD is effective in prediction of Gleason pattern lower than 5/10.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".