Quality of Life and Wellbeing Following Treatment for AML, and the Co-design of Community-based Care Plans
Bibliographic record
Abstract
IntroductionAcute Myeloid Leukemia (AML) is a form of blood cancer. Treatment for AML requires intensive chemotherapy and can have lasting impacts on the lives of patients due to potential late adverse effects. Clinical and patient-reported outcomes, such as quality of life (QoL) and wellbeing, may be improved with patient-centered community-based care plans.The study purpose is to:1)tGain insight on aspects of QoL and wellbeing that matter to patients 2)tPartner with patients to design collaborative care plans for community-based settings MethodsA pilot study was recently launched (May, 2019) at the Vancouver General Hospital with a goal to recruit 50 people with AML. Eligible patients are visited in hospital 7-10 days following initiation of treatment, and again 60 days later. At each time point, a survey comprising demographic questions and standardized questionnaires (EORTC QLQ-C30, ICECAP-A, and EQ-5D-5L) is administered, followed by a debriefing interview. The debriefing interview allows us to engage patients by asking them their opinion on the clarity and relevance of the questionnaires, what aspects of QoL and wellbeing matter most to them, and whether they would be interested in being part of the research team in the future. Results An interim analysis on the results from the debriefing interviews will be presented, which will include patient feedback on the questionnaires and their interest in joining the research team. 90% of the patients we invite have joined the study and early results suggesting that most people are interested in becoming more involved as research partners. Discussion/Implications The results from the debriefing interviews will be used to inform the design of the future scale-up version of the study. Dissemination plan/KT approachThe knowledge gained from this study will be shared with physicians through publications in scientific journals and at conferences. Acknowledgements (of funders, supporters)This study is being conducted by BC Cancer and the Leukemia/Bone Marrow Transplant Program of BC, and is sponsored by the VGH and UBC Hospital Foundation and the Canadian Centre for Applied Research in Cancer Control (ARCC). ARCC is funded by the Canadian Cancer Society Research Institute.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".