Bibliographic record
Abstract
Immunotherapy has become a research hotspot in the field of tumor therapy due to its significant therapeutic effects. Since the discovery of PD-1/PD-L1, this pair of immune cell receptors and ligands has rapidly become an important area of research in tumor immunity and even immunology in general. Targeted drugs against PD-1/PD-L1 have also become an essential part of immunotherapy and provided new life opportunities for countless cancer patients. This article will review the background and significance of the discovery of PD-1/PD-L1, and systematically introduce their applications and related inhibitor drugs. On the other hand, although PD-1 and PD-L1 discoveries have had a groundbreaking impact on immunology, there are still some shortcomings and uncharted territories for their related targeted drugs. This article will show some of the drawbacks of the functions of PD-1/PD-L1 inhibitor drugs and future views of PD-1/PD-L1 related immune suppression treatments, in order to further understand its mechanism and provide new ideas for clinical treatment of malignant tumors.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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 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".