A fork in the road: A mixed methods study exploring why older adults with acute myeloid leukemia choose different treatment paths.
Bibliographic record
Abstract
7520 Background: Current treatment options for acute myeloid leukemia (AML) are diverse, including intensive chemotherapy (IC), low intensity therapy, best supportive care (BSC), and hospice care. Despite continued development of new therapies, recent data suggest that approximately 60% of older US patients remain untreated, but reasons for this are not well understood. By gathering insights from physicians, patients, and their family members, this study aims to better understand the factors that influence treatment decisions for adults with AML. Methods: Physicians in the US (n=4), UK (n=3) and Canada (n=3), and 15 US AML patient-family member dyads took part in one-on-one, 60-minute semi-structured interviews. Each participant rated a series of factors on a scale from 0 (not at all important) to 3 (very important) to determine their importance in treatment decision-making. Among the 15 adults with AML (>65 years, not taking IC) interviewed thus far, 13 had not received any treatment. Additional interviews are scheduled in the UK and Canada with patients having varied treatment experiences (data will be available for presentation). Results: To date, findings highlight the key role perceptions of side effects and patient health play in treatment decision making. A fear of treatment side effects was the primary reason patients (n=9/13) opted not to receive treatment. For the 2/15 study patients who had received treatment, side effects were considered the worst part of their treatment experience. Physicians also stated patients on BSC would be more willing to take low intensity treatments if risks (e.g., side effects) were minimized. Patients (n=11/15), their family members (n=11/15), and physicians (n=10/10) agreed that patients’ health (including age and comorbidities) influenced if treatment was pursued. Additionally, US physicians suggested that some patients have little desire to pursue treatment, with patients’ perception of low intensity therapy having poor efficacy and proximity of care influencing their choice for BSC or hospice care. Further analysis will explore other factors influencing patients’ treatment decisions and differences among patients who receive treatment versus those who do not. Conclusions: The treatment decision-making process for older adults with AML is complex and multifactorial. Understanding factors that influence treatment decisions is important if drug developers and prescribers are to ensure the availability of therapies that better align with individual patients’ needs.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".