Design of Tumor Biomarker–Monitoring Trials: A Proposal by the European Group on Tumor Markers
Bibliographic record
Abstract
A major application of tumor biomarkers is in serial monitoring of cancer patients, but there are no published guidelines on how to evaluate biomarkers for this purpose. The European Group on Tumor Markers has convened a multidisciplinary panel of scientists to develop guidance on the design of such monitoring trials. The panel proposes a 4-phase model for biomarker-monitoring trials analogous to that in use for the investigation of new drugs. In phase I, biomarker kinetics and correlation with tumor burden are assessed. Phase II evaluates the ability of the biomarker to identify, exclude, and/or predict a change in disease status. In phase III, the effectiveness of tumor biomarker-guided intervention is assessed by measuring patient outcome in randomized trials. Phase IV consists of an audit of the long-term effects after biomarker monitoring has been included into standard patient care. Systematic well-designed evaluations of biomarkers for monitoring may provide a stronger evidence base that might enable their earlier use in evaluating responses to cancer therapy.
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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 | 0.405 | 0.271 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.014 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.049 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 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".