Revaccination following CAR-T therapy: a needs assessment
Bibliographic record
Abstract
BACKGROUND: CD19-targeted chimeric antigen receptor T-cell (CAR-T) therapy has transformed treatment for relapsed/refractory large B-cell lymphoma, offering promising remission rates. However, it carries significant infectious risks, necessitating revaccination for infection prevention. METHODS: This single-center quality improvement study aimed to (1) assess revaccination uptake and barriers in CAR-T recipients through interviews and focus groups, and (2) use qualitative insights to develop an educational handout to improve revaccination adherence. The study was conducted at Juravinski Hospital and Cancer Centre, Hamilton, Ontario, and enrolled 22 patients who received CAR-T between January 2020 and June 2023. Participants completed a survey evaluating their revaccination experience, which informed the development of a patient-facing educational handout. A focus group of survey participants then reviewed the handout and offered feedback on its clarity and usefulness. RESULTS: Only 50% of participants proceeded with revaccination. Key barriers included limited awareness among primary care providers (PCPs), poor communication between hematologists and PCPs, and logistical difficulties. The focus group highlighted gaps in understanding post-CAR-T immunization needs and emphasized the importance of patient and physician education. Participants supported the creation of a concise handout outlining the revaccination schedule and clarified the PCP's role in coordination. CONCLUSION: This study underscores the need for improved communication across care providers and accessible patient education to support post-CAR-T revaccination. Future efforts should focus on implementing and evaluating targeted educational tools to enhance vaccine uptake in this high-risk population..
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".