Current Trends and Outcomes in Cellular Therapy Activity in the United States, Including Prospective Patient-Reported Outcomes Data Collection in the Center for International Blood and Marrow Transplant Research Registry
Bibliographic record
Abstract
The Center for International Blood and Marrow Transplant Research (CIBMTR) prepares an annual set of summary slides to summarize the trends in transplantation and cellular therapies. For the first time in the 2023 summary slides, the CIBMTR incorporated data for patients receiving chimeric antigen receptor T cell (CAR-T) infusions. In addition, data on patient-reported outcomes (PROs) are included. This report aims to update the annual trends in US hematopoietic cell transplantation (HCT) activity and incorporate data on the use of CAR-T therapies. A second aim is to present and describe the development, implementation, and current status of PRO data collection. In August 2020, the CIBMTR launched the Protocol for Collection of Patient-Reported Outcomes Data (CIBMTR PRO Protocol). The CIBMTR PRO Protocol operates under a centralized infrastructure to reduce the burden to centers. Specifically, PRO data are collected from a prospective convenience sample of adult HCT and CAR-T recipients who received treatment at contributing centers and consented for research. Data are merged and stored with the clinical data and used under the governance of the CIBMTR Research Database Protocol. Participants answer a series of surveys developed by the Patient Reported Outcomes Measurement Information System (PROMIS) focusing on physical, social and emotional, and other measures assessing financial well-being, occupational functioning, and social determinants of health. To complement traditionally measured clinical outcomes, the surveys are administered at the same time points at which clinical data are routinely collected. As of September 2023, PRO data have been collected from 993 patients across 25 different centers. With the goal of incorporating these important patient perspectives into standard clinical care, the CIBMTR has added the PRO data to Data Back to Centers (DBtC). Through expanding the data types represented in the registry, the CIBMTR aims to support holistic research accounting for the patients' perspective in improving patient outcomes. CIBMTR PRO data aim to provide a foundation for future large-scale, population-level evaluations to identify areas for improvement, emerging disparities in access and health outcomes (eg, by age, race, and ethnicity), and new therapies that may impact current treatment guidelines. Continuing to collect and grow the PRO data is critical for understanding these changes and identifying methods for improving patients' quality of life.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".