Bibliographic record
Abstract
A wide spectrum of non-protein based biomarkers are under development that promises to revolutionize the care of prostate cancer (CaP) patients. In the context of CaP detection we highlight the potential value of the urine tests PCA3 and Prostarix(TM), especially for their ability to stratify patient risk with previous negative biopsy for occult cancer. The search for such markers is made more complex by the development of MRI and image-fusion technology that can help focus biopsy on specific prostatic lesions. Tissue-gene signatures are finding utility in predicting recurrence and progression after radical prostatectomy or identifying patients with apparent low-risk disease who may harbor occult higher-risk disease that would warrant definitive intervention over active surveillance. Furthermore, serum-based microRNA, cell-free DNA and circulating tumor cells are under investigation in clinical trials, especially in the setting of metastatic castration-resistant CaP, for their ability to predict response to novel therapies and patient survival. The meticulous testing of these biomarkers by incorporation into current clinical trials will aid in their widespread use and ability to guide CaP management.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".