Patient-Centered Insights on Treatment Decision Making and Living with Acute Myeloid Leukemia and Other Hematologic Cancers
Bibliographic record
Abstract
Expectations relating to treatment and survival, and factors influencing treatment decisions are not well understood in adult patients with acute myeloid leukemia. This study analyzed combined findings from a targeted literature review with patient-reported information shared on YouTube to further understand patient perspectives in hematologic cancers and, in particular, acute myeloid leukemia. The targeted literature review included articles concerning patient (aged ≥ 18 years) experiences or perspectives in acute myeloid leukemia or other hematologic cancers. YouTube video selection criteria included patients (aged ≥ 60 years) with self-reported acute myeloid leukemia. In total, 26 articles (13 acute myeloid leukemia-specific and 14 other hematologic cancers, with one relevant to both populations) and 28 videos pertaining to ten unique patients/caregivers were identified. Key concepts reported by patients included the perceived value of survival for achieving personal and/or life milestones, the emotional/psychological distress of their diagnosis, and the uncertainties about life expectancy/prognosis. Effective therapies that could potentially delay progression and extend life were of great importance to patients; however, these were considered in terms of quality-of-life impact and disruption to daily life. Many patients expressed concerns regarding the lack of treatment options, the possibility of side effects, and how their diagnosis and treatment would affect relationships, daily lives, and ability to complete certain tasks. Both data sources yielded valuable and rich information on the patient experience and perceptions of hematologic cancers, in particular for acute myeloid leukemia, and its treatments. Further understanding of these insights could aid discussions between clinicians, patients, and their caregivers regarding treatment decisions, highlight outcomes of importance to patients in clinical studies, and ultimately, inform patient-focused drug development and evaluation.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".