Impact of Chimeric Antigen Receptor T-Cell Therapy on Health Utilities of Patients Diagnosed with Diffuse Large B-Cell Lymphoma in Canada
Bibliographic record
Abstract
Chimeric antigen receptor T-cell (CAR T) therapy has been shown to improve survival for patients with diffuse large B-cell lymphoma (DLBCL), but it comes at a high cost. Few studies have assessed the impact of CAR T on the health utilities of patients outside clinical trials. This information is important for economic evaluations aimed at determining the value for money of CAR T therapy and for guiding patient care decisions. This objective of this study was to evaluate the impact of CAR T-cell therapy on the health utilities of patients diagnosed with relapsed/refractory (r/r) DLBCL. A prospective, longitudinal study was conducted at Princess Margaret Cancer Centre (Toronto) from April 2022 to March 2023. Patients were assessed at baseline, 2 wk, 3 mo, and 6 mo post-treatment. Patients completed the EQ-5D-5L and EQ-5D-5L visual analog scale (VAS), the EORTC QLQ-C30, and a clinical data form. Using a Canadian valuation algorithm and population weights, the EQ-5D-5L and EORTC QLQ-C30 were converted to utility values. The EORTC QLQ C-30 was converted to the Quality-of-Life Utility-Core 10 dimensions (QLU-C10D). Descriptive analyses were conducted for each assessment, the mean utility scores and mean change from baseline were calculated. We used weighted generalized estimating equations to examine the predictors of health utility values. A total of 55 patients were treated with CAR T (mean age 58 yr, 55% male). The baseline scores were 0.82 ± 0.12 (EQ-5D-5L), 0.67 ± 0.23 (QLU-C10D), and 74 ± 17 (VAS). The mean change from baseline and 2 wk post-treatment was -0.044, -0.163, and -8.54, respectively. From baseline to 6 mo post-treatment, the change was -0.053, -0.002, and 3.3, respectively. Progression status was a significant predictor of utility scores across all instruments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".