A tumormarker-kutatás rövid története. A PSA, mint legismertebb urológiai tumormarker felfedezésének története
Bibliographic record
Abstract
There are a number of tumour markers available today enabling the early detection of malignancies thus saving the lives of many thousands of people worldwide. By definition, tumour markers are substances in tissues, blood, bone marrow or other body fluids that appear in cancer patients’ samples or are present at significantly elevated levels compared to normal conditions. The first marker of malignant disease in modern medicine was identified in 1846 by the English physician-chemist Henry Bence-Jones. Yet, at that time, of course, he was not aware that the protein (named as Bence-Jones) he discovered, was a pathogenic indicator of multiple myeloma. The classic era of tumour markers started in the 1960s with the discovery of two leading oncofetal antigens, alpha-fetoprotein (AFP) and carcinoembryonic antigen (CEA). AFP was published in 1944 by Swedish scientist Kai O. Pedersen, while the discovery of CEA is associated with two Canadian physicians, Phil Gold and Samuel O. Freedman. One of the most widely known tumour markers indicating prostate cancer is the prostate-specific antigen or PSA. The discovery of PSA as a clinically useful marker of prostate cancer and its translation into clinical practice can be attributed to Ming C. Wang, who described the molecule in 1979. The US Food and Drug Administration (FDA) approved PSA in 1986 as a molecule for monitoring the disease, and in 1994 it licenced the measuring of PSA levels as a screening test, thus facilitating the early detection of prostate cancer and enabling more effective treatment.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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; both teacher heads agree on what is shown here.
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".