Mitigating inequity: ethically prioritizing patients for CAR T-cell therapy
Bibliographic record
Abstract
Manufacturing capacity and institutional infrastructure to deliver chimeric antigen receptor T-cell therapies (CAR-T) are pressured to keep pace with the growing number of approved products and expanding eligible patient population for this potentially life-saving therapy. Consequently, many cell therapy programs must make difficult decisions about which patient should get the next available treatment slot. This situation requires an ethical framework to ensure fair and equitable decision-making. In this perspective, we discuss the application of Accountability for Reasonableness (A4R), a priority-setting framework grounded in procedural justice, to the problem of limited CAR-T slots at our institution. We formed a multidisciplinary working group spanning several hematological malignancies. Through multiple rounds of partner engagement, we used A4R guiding principles to identify 4 main criteria to prioritize patients for CAR-T: medical benefit, safety/risk of complications, psychosocial factors, and medical urgency. Associated measures/tools and an implementation process were developed. We discuss further how ethical principles of fairness and equity demand a consistent approach within health systems that does not disadvantage medically underserved or underrepresented populations and supports overcoming barriers to care. In our commitment to transparency and collaboration, we make our tools available to others, ideally to be used to engage in their own A4R process, adapting the tools to their unique environments. Our hope is that our preliminary work will support the advancement of further study in this area globally, aiming for justice in resource allocation for all potential CAR-T candidates, wherever they may seek care.
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 | 0.114 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.020 |
| Scholarly communication | 0.015 | 0.009 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.005 | 0.014 |
| 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".