Bibliographic record
Abstract
PURPOSE OF REVIEW: To discuss the important advances in CAR T cell therapy over the past year, focusing on clinical results where available. RECENT FINDINGS: Approximately 30 years after they were first conceived of and 15 years after the first small-scale single-center clinical trials, the past 3 years represent a major milestone in the development of CAR T cells. In the United States, the Food and Drug Administration (FDA) approved Tisagenlecleucel for the treatment of relapsed/refractory B-ALL and Axicabtagene Ciloleucel, for adults with relapsed/refractory diffuse large B cell lymphoma (R/R DLBCL) in 2017. Tisagenlecleucel received a second indication in adults with R/R DLBCL in 2018. Regulatory approval for CAR T cells was then granted in Europe, Canada, Australia, and Japan. Most recently, in July 2020 the FDA granted regulatory approval to a third CAR T cell product, Brexucabtagene Autoleucel for mantle cell lymphoma. All products target the CD19 antigen but differ in the costimulatory molecule within the CAR construct. Currently, it is unknown whether there are any differences in clinical activity or toxicity between these products. SUMMARY: The CAR T cell the platform is evolving at a rapid pace and is expected to further improve the therapeutic outcomes of hematological 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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.006 | 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".