Cellular therapy: Great promise, but at what cost?
Bibliographic record
Abstract
The introduction of chimeric antigen receptor T cell (CAR-T) therapy has been transformative in hematological malignancies. In the US, there are now 6 FDA-approved products across 5 malignancies; acute lymphoblastic leukemia, diffuse large B cell lymphoma (DLBCL), follicular lymphoma, mantle cell lymphoma, and multiple myeloma. These are highly potent treatments that are effective in patients that are highly pretreated and chemo-refractory and are likely curative in some cases. However, despite the fact that the first commercial CAR-T product (axicabtagene ciloleucel) received an FDA approval in October 2017, access to CAR-T remains challenging for myriad reasons including, but not limited to, expertise, infrastructure for apheresis and administration, manufacturing capacity, and cost. In particular, the cheapest CAR-T product is $373,000 USD. Even in a world where we have become accustomed to targeted therapies costing well over $10,000/month, these prices are extraordinarily high.
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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.008 |
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".