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.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".