Understanding Goals of Care for Patients Undergoing Chimeric Antigen Receptor T-Cell Therapy: A Qualitative Descriptive Study
Bibliographic record
Abstract
Background: Chimeric Antigen Receptor (CAR) T-cell therapy is a relatively novel treatment in Canada for relapsed and refractory leukemias and lymphomas. Limited evidence is available on patient experiences when undergoing this treatment, and there is no research regarding patients’ goals of care (GOC) when undergoing this treatment. Aims: This study aimed to explore patients’ experiences undergoing CAR T-cell therapy, particularly as it related to their GOC. Methods: A qualitative descriptive approach was employed. Information was gathered via semi-structured interviews and medical chart review. Interviews were transcribed and analyzed using content analysis to identify key themes. Results: Six regional patients participated in this study. Ten key themes were identified, highlighting patients’ identified GOC, a lack of implicit GOC discussions, challenging transitions throughout care, and a lack of nursing involvement in GOC discussions. Conclusion: Patients undergoing CAR T-cell therapy have clearly identified GOC (simply to survive) but have not explicitly discussed these goals with their healthcare providers. Overall, patients had positive experiences in receiving care during CAR T-cell therapy but there was opportunities identified to improve care, related to facilitating GOC discussions, increasing support during transitions in care, and optimizing the role of the nurse within GOC conversations. Future research should aim to investigate the experiences of a more varied group of patients including those who were offered and declined receiving CAR T-cell therapy, the perceptions of healthcare providers regarding GOC discussions within CAR T-cell therapy, and the role of nurses in GOC discussions.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".