Carcinoembryonic Antigen, Carbohydrate Antigen 19-9, Cancer Antigen 125, Prostate-Specific Antigen and Other Cancer Markers: A Primer on Commonly Used Cancer Markers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cancer markers are molecules produced by cancer cells which may serve to identify the presence of cancer. Cancer markers can be differentiated as serum-based, radiology-based and tissue-based, and are one of the most important tools in diagnosing, staging and monitoring of treatment of many cancers. The most used cancer markers are serum cancer markers due to its relative ease and lower cost of testing. However, serum cancer markers have poor mass screening utilization due to poor positive predictive value. Several markers such as prostate-specific antigen (PSA), beta-human chorionic gonadotropin (B-hCG), alpha-fetoprotein (AFP), and lactate dehydrogenase (LDH) are used to aid in diagnosis of cancer in cases of high suspicion. Serum markers such as carcinoembryonic antigen (CEA), AFP, carbohydrate antigen 19-9 (CA 19-9), and 5-hydroxyindoleacetic acid (5-HIAA) play a significant role in assessing disease prognosis as well as response to treatment. This work reviews the role of some of the biomarkers in the diagnosis and treatment of cancer.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it