Half-Century of Cancer Biomarkers: Lessons from the Past and Projections for the Future
Bibliographic record
Abstract
A handful of cancer biomarkers are used widely in clinical practice, mainly for aiding in diagnosis and for monitoring therapy. These biomarkers include α-fetoprotein (AFP),4 carcinoembryonic antigen (CEA), prostate-specific antigen, and the carbohydrate antigens CA125, CA19.9, and CA15.3. Other, more specialized cancer biomarkers are used less frequently. Molecular/genomic markers will not be discussed here. An examination of the history of these biomarkers reveals that they were discovered in the mid-1960s and 1970s (AFP, CEA) or in the 1980s, thanks to the monoclonal antibody technology revolution. The success of these markers in clinical practice sparked interest to discover new cancer biomarkers that could be applied for population screening, early diagnosis, …
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".