Bibliographic record
Abstract
Advanced cellular therapies have been introduced in Canada over the past two years. Chimeric antigen receptor (CAR) T-cell therapy is the current standard of care for third-line large B-cell lymphoma (LBCL), relapsed/ refractory (RR) acute lymphoblastic leukemia (ALL) in patients < 26 years old and, more recently, in third-line mantle cell lymphoma. These novel therapies are now gaining more prominence in the treatment of LBCL with recent FDA approval for the second line in patients eligible for stem cell transplant, based on recent Phase 3 trials. Another class of novel immunotherapy agents are bispecific T-cell engagers (BiTEs) which have been studied in many B-cell malignancies but are not yet approved in Canada. The indolent non-Hodgkin’s lymphoma (iNHL) and chronic lymphocytic leukemia (CLL) landscape have been evolving over the past few years with many novel therapies being studied and becoming available. However, patients with RR iNHL, as well as patients using Bruton tyrosine kinase (BTK) and B-cell lymphoma-2 (BCL2) inhibitors for refractory CLL continue to have an unmet need for treatment. This article will focus on cellular therapy that will likely be available for use by Canadian clinicians in the near future to treat patients with iNHL and CLL.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".