Identification of a Patient Suitable for CAR-T Cell Therapy in the Outpatient Setting: A Vodcast and Case Example
Bibliographic record
Abstract
Chimeric antigen receptor T cell (CAR-T) therapies targeting the CD19 antigen have been associated with high and durable response rates in patients with diffuse large B cell lymphoma (DLBCL). CAR-T cell therapies are commonly administered in the inpatient setting due to the average onset of cytokine release syndrome within the first 3 days post infusion, but there has been growing interest in delivering CAR-T cell therapies in the outpatient setting to overcome frequent hospital bed shortages and the high cost of inpatient care. Although this approach could improve access whilst catering to patient preference, it requires a multidisciplinary approach as well as careful patient selection. Herein, Dr. Foley and Dr. Kuruvilla discuss the case of a patient presenting with the ideal profile for CAR-T cell therapy referral whilst also determining the key attributes for eligibility from a clinician's perspective. Solutions for successful outpatient management include proper education, caregiver support, and early referral to ensure a timely infusion. In conclusion, outpatient administration of CAR-T cell therapy in patients with DLBCLs should be assessed on a case-by-case basis.A vodcast feature is available for this article.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| 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".