PSA density improves prediction of prostate cancer.
Bibliographic record
Abstract
INTRODUCTION: Prostate-specific antigen (PSA) and the digital rectal exam (DRE) have moderate sensitivity but low specificity for cancer diagnosis, potentially causing unnecessary treatment complications with prostate biopsy. Transrectal ultrasound (TRUS) to evaluate prostate size and calculate PSA density can improve the specificity of PSA in predicting cancer. We evaluated the sensitivity and specificity of different pre-biopsy tests to detect prostate cancer. MATERIALS AND METHODS: Pre-biopsy data were collected from 521 men referred for biopsy from January-December 2011 and cancer aggressiveness data from 96 men who had radical prostatectomy. Model predictors included total PSA, DRE, the ratio of free to total PSA (PSAf/t), and PSA density. We used logistic regression and ROC curve analyses to compare the accuracy of different models to predict positive biopsy. RESULTS: The area under the curve (AUC) for model A (PSA total, DRE, PSAf/t) was moderate, but significant (AUC = .59, p < .05); only PSAf/t was a significant independent predictor of positive biopsy (OR = .002, p < .05). In model B (PSAf/t and PSA density; AUC= .66, p < .05), PSA density was the only strong predictor (OR = 1067.93, p < .05). Both models had comparable sensitivity (74% versus 72%) but model B had greater specificity (44% versus 61%). PSA density was also a significant predictor of different indices of aggressive cancer. CONCLUSIONS: PSA density has discriminative predictive power for prostate cancer. It had similar sensitivity, but greater specificity compared to using PSA total, DRE and PSAf/t. These results support the value of using PSA density to improve prediction of prostate cancer and reduce unnecessary biopsies.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".