Emerging Biomarkers for the Diagnosis and Prognosis of Prostate Cancer
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Early detection of prostate cancer (CaP), the most prevalent cancer and the second-leading cause of death in men, has proved difficult, and current detection methods are inadequate. Prostate-specific antigen (PSA) testing is a significant advance for early diagnosis of patients with CaP. CONTENT: PSA is produced almost exclusively in the prostate, and abnormalities of this organ are frequently associated with increased serum concentrations. Because of PSA's lack of specificity for CaP, however, many patients undergo unnecessary biopsies or treatments for benign or latent tumors, respectively. Thus, a more specific method of CaP detection is required to augment or replace screening with PSA. The focus recently has been on creating cost-effective assays for circulating protein biomarkers in the blood, but because of the heterogeneity of CaP, it has become clear that this effort will be a formidable challenge. Each marker will require proper validation to ensure clinical utility. Although much work has been done on variations of the PSA test (i.e., velocity, density, free vs bound, proisoforms) with limited usefulness, there are many emerging markers at various stages of development that show some promise for CaP diagnosis. These markers include kallikrein-related peptidase 2 (KLK2), early prostate cancer antigen (EPCA), PCA3, hepsin, prostate stem cell antigen, and alpha-methylacyl-CoA racemase (AMACR). We review biomarkers under investigation for the early diagnosis and management of prostate cancer. SUMMARY: It is hoped that the use of panels of markers can improve CaP diagnosis and prognosis and help predict the therapeutic response in CaP patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 it