Prostate specific antigen density with a cut-off point of 0.12 is the best predictor of cancer on prostate biopsies
Why this work is in the frame
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Bibliographic record
Abstract
Introduction: Prostate cancer (PCa) is the second most frequent type of cancer in men; diagnosis is reached through prostate biopsy, an invasive procedure, so efforts are made to avoid unnecessary ones by improving them and optimizing current biomarkers. Among existing biomarkers, prostate -specific antigen (PSA) Density (PSAD), a PSA derivative, is considered a feasible biomarker for PCa Objective: This study aimed to evaluate different PSA derivatives as well as determine whether PSAD with a cut-off point of 0.12 was more accurate than the widely used value of 0.15 to recognize patients suffering from prostate cancer. Material and methods: Retrospective study of 391 patients aged 40 years or more, who underwent prostate biopsy at Fundacion Urologica Cordoba para la Docencia e Investigacion Medica (FUCDIM) , from November 2010 to July 2014. Sensitivity and specificity for the PSAD cut-off point of 0.12 were estimated. Diagnostic accuracy was evaluated through Receiver Operating Characteristic (ROC) curves and the Area Under the Curve. PSAD was compared to total PSA, free/total PSA index, and (free/total PSA)/PSAD. Results: Significantly higher mean values were found in terms of total PSA, PSAD, and F/T PSA in patients with confirmed diagnoses of PCa. PSAD with a cut-off point greater than 0.12 detected a significantly higher percentage of cancer, 80%, p=0.0001, compared to the control group. Furthermore, the strength of association determined that patients with PSAD greater than 0.12 were 3 times more likely to belong to the PCa group. Conclusion: Total PSA, PSAD, and F/T PSA in patients with cancer yielded significantly higher mean values. The best biomarker to predict prostate cancer by biopsy was PSAD, with a cut-off point of 0.12. These results indicate the need to develop further investigations in diverse geographic areas to define the best local cut-off points for PSAD. Our results demonstrate that the established cut-off point of 0.15 could be appropriate for European men but appears to be too high for American and low for Asian males.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it