Harnessing the Immune System: An Effective Way to Manage Diffuse Large B-Cell Lymphoma
Bibliographic record
Abstract
Diffuse large B-cell lymphoma (DLBCL) is a heterogenous hematological disorder with malignant potential controlled by immunological characteristics of the tumor microenvironment. Rapid breakthrough in the molecular pathways has made immunological approaches the main anchor in the management of DLBCL, with or without chemotherapeutic agents. Rituximab was the first monoclonal antibody approved for the treatment of DLBCL. Following rituximab that transformed the therapeutic landscape, other novel immunological agents including chimeric antigen T-cell therapy have reshaped the management of relapsed/refractory DLBCL. However, resistance and refractory state remain a challenge in the management of DLBCL. For this literature review, we screened articles from Medline, Embase, Cochrane databases and the European/North American guidelines from March 2010 through October 2022 for DLBCL. Here we discuss immunological agents that will significantly affect future treatment of this aggressive type of lymphoma.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".