Bibliographic record
Abstract
BACKGROUND: Biological and technical advances have led to greatly increased research and development of cancer biomarkers. This overview lists some of the challenges and barriers to developing novel effective cancer biomarkers and enablers to facilitate cancer biomarker development. METHODS: Current scientific literature regarding development of biomarkers for cancer and other diseases was reviewed. RESULTS: Challenges to developing cancer biomarkers include better understanding of biological heterogeneity, including host/tumor heterogeneity; analytical factors, such as interferences and analytical sensitivity; clinical pathologic factors, such as current histopathologic standards; and health service and market factors. More standardized biomarker definitions, standardization of cancer biology terminology, and high-quality reference materials (specimen and clinical data repositories) were identified as factors required to support advances in cancer biomarkers. CONCLUSIONS: With the above enablers, novel cancer biomarkers may be useful, both for assessing early and established neoplasia more precisely and for contributing data toward development of novel practical concepts regarding cancer biology.
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 | 0.024 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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".