Practice gaps and barriers to optimal care of hematologic malignancies in the United States
Bibliographic record
Abstract
BACKGROUND: Treating patients with hematologic malignancies can be challenging for physicians because of the rapidly evolving standards of care and relatively low incidence of these diseases. OBJECTIVE: To identify clinical challenges among hematologists and medical oncologists regarding the provision of care to patients with chronic myeloid leukemia (CML), acute lymphoblastic leukemia (ALL), or B-cell lymphomas. Methods Hematologists and medical oncologists in active practice in the United States and who have a case load of ≥ 1 patient a year with CML, ALL, or B-cell lymphoma were recruited. The initial qualitative phase consisted of an online case-based survey followed by an interview exploring the contextual and behavioral factors that influence treatment decisions (n = 27). The analysis of qualitative data then informed a quantitative phase, in which 121 participants completed an online survey composed of case vignettes, multiple choice, and semantic differential rating scale questions. The respondents' answers were compared with recommendations from treatment guidelines and faculty experts. RESULTS: A higher frequency of bone marrow biopsies was reported compared with expert faculty recommendations by 74% of oncologists. Many respondents failed to recognize the clinical relevance of BCR-ABL mutations other than T315I. Respondents reported perceiving difficulties in individualizing treatment and interpreting response to treatment in patients with ALL and B-cell lymphomas. Fewer than 30% of respondents recognized the mechanisms of action of 5 of the 9 promising investigational agents presented. LIMITATIONS: Participant self-selection bias is a possibility because participation was voluntary. Practice gaps are not based on clinical data, but hypothetical case situations and self-report. CONCLUSIONS: Findings from this study can guide education to address the identified challenges in caring for patients with hematologic malignancies and improving patient care. FUNDING: This needs assessment was financially supported with an educational research grant from Pfizer Medical Education Group to the Annenberg Center for Health Sciences at Eisenhower.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".