Individual risk prediction of high grade prostate cancer based on the combination between total prostate-specific antigen (PSA) and free to total PSA ratio
Bibliographic record
Abstract
OBJECTIVES: Clinical practice guidelines endorse the stratification of prostate cancer (PCa) risk according to individual total prostate-specific antigen (tPSA) values and age to enhance the individual risk-benefit ratio. We defined two nomograms to predict the individual risk of high and low grade PCa by combining the assay of tPSA and %free/tPSA (%f/tPSA) in patients with a pre-biopsy tPSA between 2 and 10 μg/L. METHODS: The study cohort consisted of 662 patients that had fPSA, tPSA, and a biopsy performed (41.3% with a final diagnosis of PCa). Logistic regression including age, tPSA and %f/tPSA was used to model the probability of having high or low grade cancer by defining 3 outcome levels: no PCa, low grade (International Society of Urological Pathology grade, ISUP<3) and high grade PCa (ISUP≥3). RESULTS: The nomogram identifying patients with: (a) high vs. those with low grade PCa and without the disease showed a good discriminating capability (∼80%), but the calibration showed a risk of underestimation for predictive probabilities >30% (a considerable critical threshold of risk), (b) ISUP<3 vs. those without the disease showed a discriminating capability of 63% and overestimates predictive probabilities >50%. In ISUP 5 a possible loss of PSA immunoreactivity has been observed. CONCLUSIONS: The estimated risk of high or low grade PCa by the nomograms may be of aid in the decision-making process, in particular in the case of critical comorbidities and when the digital rectal examinations are inconclusive. The improved characterization of the risk of ISUP≥3 might enhance the use for magnetic resonance imaging in this setting.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".