Bibliographic record
Abstract
Scientific innovation has promoted the rapid discovery and development of cancer-related biomarkers. This has fostered improved mechanistic understandings of disease biology, facilitated the development of novel targeted therapies and offers the potential to better define the natural history of a disease and to predict response to specific treatment. Clinical trials are evolving, from the use of exploratory 'correlative studies' that assist in the understanding of disease mechanisms, to the use of validated biomarkers that are integral to the design of definitive prospective studies. A rigorous process for biomarker development and validation, followed by evaluation in well-designed, prospective clinical trials that demonstrate improved patient outcomes is required for new biomarkers to change clinical practice. To date, the number of biomarkers considered clinically useful is disappointingly small because of conflicting conclusions generated from trials with practical and methodological limitations. This review will highlight several important aspects related to the design and analysis of clinical trials that incorporate a biomarker as a central component. First, we review biomarker development, how biomarkers may be used as targets and how biomarkers can influence methodology to optimize efficiency of drug development through the use of surrogate outcomes. Second, we focus on issues related to trials evaluating potential prognostic biomarkers and how the predictive properties of biomarkers may be used to determine which patients might optimally benefit from a specific intervention.
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 | 0.444 | 0.628 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".