Bibliographic record
Abstract
Cancer consistently ranks among the top causes of death worldwide. Traditional therapies include surgery, chemotherapy, and radiation. However, many patients either do not respond to or develop specific treatment resistance. Given the advent of genome, transcriptome, and proteome technologies, personalized medicine has gained tremendous recognition as a therapeutic field. Antibodies are Y-shaped proteins produced by activated B immune cells, featuring two substrate recognition sites. They identify and bind to invading pathogens, such as bacteria, viruses, and toxins to help prevent and eliminate infections. The concept and potential of therapeutic monoclonal antibodies (mAbs) in cancer was put forth by Paul Ehrlich over a century ago. A breakthrough by Köhler and Milstein (Cambridge, 1975) followed the development of hybridoma technology, allowing mass production of specific antigen-induced mAbs. MAbs exert their effect through various mechanisms to combat malignancies. Depending on their design, mAbs bind to specific cognate cell-surface receptors to modulate cell growth, apoptosis, and immune recognition. MAbs targeting various cancer types have received clinical approval. For instance, rituximab binds CD20 expressed on B cell non-Hodgkin lymphoma (NHL) and chronic lymphocytic leukemia (CLL), leading to immune-mediated target cell destruction. A class of mAbs known as immune checkpoint inhibitors (ICIs), activate the body's natural immune response against tumor cells by blocking their suppression. Notable ICI include pembrolizumab and nivolumab, which specifically target the programmed cell death receptor-1 (PD-1). Unlike traditional therapies which often cause damage to healthy cells, mAbs are intrinsically target-specific, reducing or eliminating harmful side effects. As such, they have emerged to provide more effective and less toxic options for cancer patients.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.032 |
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".