Prostate specific antigen density with a cut-off point of 0.12 is the best predictor of cancer on prostate biopsies
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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".