Cancer Biomarker Concentration Changes During Tumor Progression.
Bibliographic record
Abstract
Introduction: Most circulating cancer and other disease biomarker concentrations increase during disease progression, roughly correlating with tumor burden or disease severity. During the biomarker discovery phase, several studies (some published in high-impact journals) report decreases in serum biomarkers at the time of disease diagnosis or during progression (in comparison to control, non-diseased populations). It is suggested these biomarker decreases between normal and diseased populations may have utility in diagnostics. Methods: We briefly examine if a serum cancer biomarker concentration is likely to decrease as cancer progresses through empirical data. Results: We propose a simple model, which, if correct, would suggest that in most cases, the biomarker decrease during disease progression could be an artifact or epiphenomenon (thus representing false discovery). Our suggestion is supported by the very few examples of decline of serum biomarkers during cancer development and progression. Conclusions: The notion that a serum biomarker concentration could be inversely associated with tumor burden seems to be an epiphenomenon.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".