Immune checkpoints in hematologic malignancies: What made the immune cells and clinicians exhausted!
Bibliographic record
Abstract
Hematologic malignancies comprise a considerable part of cancers with high mortality at any age. Since the introduction of hematopoietic stem cell transplantation (HSCT), the overall survival of patients dramatically increased. The main goal of HSCT is the induction of a graft-versus-leukemia effect to eradicate the residual cancer cells and also reconstitute a healthy immune system for patients. However, relapse is a nettlesome challenge of HSCT. Like many other tumors, hematologic cancer cells induce immune exhaustion leading to immune escape and relapses after HSCT. Besides malignant cells, inhibitory cells such as tumor-associated macrophages and myeloid-derived suppressor cells express various inhibitory receptors capable of inducing exhaustion in immune cells, especially T and natural killer cells. The significance of immune checkpoint blocking in tumor regression in clinical trials led to the 2018 Nobel Prize in Physiology/Medicine. Here, we reviewed the clinical roles of immune checkpoints in hematologic malignancies and post-HSCT relapses.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".