Bibliographic record
Abstract
The management of hematologic malignancies has traditionally relied on chemotherapy regimens, many of which are still in use today. However, with advancements in the knowledge of tumour pathophysiology, therapies are continually evolving. Monoclonal antibodies against specific targets on tumour cells are now widely used to treat hematologic malignancies, either in combination with chemotherapy or as single agents. Rituximab, a monoclonal antibody against the CD20 antigen, is a good example of successful monoclonal antibody therapy that has improved outcomes for patients with B cell non-Hodgkin lymphomas. Monoclonal antibodies are now being used against the immune checkpoints that function to inhibit T cell activation and subsequent tumour eradication by those cytotoxic T cells. Such therapies enhance T cell-mediated tumour eradication and are widely successful in treating patients with solid tumours such as malignant melanoma. Now, they are slowly finding their place in the management of hematologic neoplasms. Even though, currently, immune checkpoint inhibitors are used for relapsed or refractory hematologic neoplasms, trials are ongoing to evaluate their role in frontline treatment. Our review focuses on the current use of immunotherapies in various hematologic malignancies.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; 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".