Bibliographic record
Abstract
Most candidate biomarkers are never adopted into clinical practice. The likelihood that a biomarker with good predictive properties will be incorporated into urologic decision-making and will improve patient care can be enhanced by following established principles of biomarker development. Studies should follow the REMARK guidelines, should have clinically relevant outcomes, and should evaluate the biomarker on the same patients to whom the biomarker would be applied in practice. It is also important to recognize that biomarker research is comparative: the question is not whether a biomarker provides information, but whether it provides better information than is already available. Continuous biomarkers should not be categorized above or below a fixed cutpoint: risk prediction allows for individualization of care. The risk predictions must be calibrated, that is, close to a patient’s true risk, and decision analysis is required to determine whether using the biomarker in clinical practice would change decisions and improve outcomes. Finally, impact studies are needed to evaluate how use of the biomarker in the real world affects outcomes.
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 | 0.113 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".