New blood‐based biomarkers for the diagnosis, staging and prognosis of prostate cancer
Bibliographic record
Abstract
The introduction of prostate-specific antigen (PSA) has revolutionized the detection and management of patients with prostate cancer. Despite this there has always been a concern among clinicians about the usefulness of total PSA levels as a marker for prostate cancer. We discuss the use of calculated variables and molecular forms of PSA. The precursor forms of PSA have been associated with the presence and biological behaviour of prostate cancer. With recent advances in biotechnology, e.g. high-throughput molecular analyses, many potential blood biomarkers have been identified and are currently under investigation. Given the plethora of candidate biomarkers we discuss a selected group of novel blood-based biomarkers, e.g. human glandular kallikrein, early prostate cancer antigen, insulin-like growth factors, urokinase plasminogen activators, transforming growth factor-beta, interleukin-6, chromogranin A, and prostate secretory protein. While these and other markers have shown promise in early-phase studies, no single biomarker is likely to have the appropriate degree of certainty to dictate treatment decisions. Consequently, the future of cancer prognosis might rely on small panels of markers that can accurately predict cancer presence, stage and metastasis, and serve as prognosticators, targets, and/or surrogate endpoints of disease progression and response to therapy.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".