The level of evidence for the use of biomarkers in the early detection of prostate cancer
Bibliographic record
Abstract
To systematically review the evidence for the use of PSA and other biomarkers in the early detection of prostate cancer, we searched PubMed for clinical trials and studies assessing PSA and other biomarkers in the early detection of prostate cancer, published between 2000 and May 2013 that included >200 subjects. The level of evidence (LOE) for clinical utility was evaluated using the tumor marker utility grading system. A total of 84 publications, corresponding to 70 trials and studies were selected for inclusion in this review. We attributed a level of evidence (LoE) of IA to PSA for early PCa detection, but we do not recommend its use in mass screening. Emerging biomarkers were assessed in prospective case-control and cohort studies: PCA3 (n=3); kallikreins (n=3); [-2]proPSA (n=5); fusion oncogenes (n=2). These studies used biopsy results for prostate cancer to determine specificity and sensitivity, but they did not assess the effect on PCa mortality. The LoE attributed was III-C. PSA can be used for early prostate cancer detection but mass screening is not recommended. Studies on other biomarkers suggest that they could be used, individually or in combination, to improve the selection of patients with elevated PSA levels for biopsy, but RCTs assessing their impact on prostate cancer management and mortality are needed. A better use of available tests is possible for men at risk in order to maximize the risk-benefit ratio.
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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 | 0.034 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.014 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".