Knowledge, skills, and confidence gaps impacting treatment decision making in relapsed/refractory chronic lymphocytic leukemia and mantle cell lymphoma: a quantitative survey study in France, Germany, and the United States
Bibliographic record
Abstract
BACKGROUND: With recent advancements in the treatment of chronic lymphocytic leukemia (CLL) and mantle cell lymphoma (MCL), healthcare specialists may face challenges making treatment and management decisions based on latest evidence for the optimal care of patients with these conditions. This study aimed to identify specific knowledge, skills, and confidence gaps impacting the treatment of CLL and MCL, to inform future educational activities. METHODS: Hematologists and hemato-oncologists (HCPs, n = 224) from France (academic settings), Germany, and the United States (academic and community settings) responded to a 15-minute quantitative needs assessment survey that measured perceived knowledge, skills, and confidence levels regarding different aspects of treatment and management of CLL and MCL patients, as well as clinical case questions. Descriptive statistics (cross tabulations) and Chi-square tests were conducted. RESULTS: Four areas of educational need were identified: (1) sub-optimal knowledge of treatment guidelines; (2) sub-optimal knowledge of molecular testing to inform CLL/MCL treatment decisions; (3) sub-optimal skills when making treatment decisions according to patient profile (co-morbidities, molecular testing results); and (4) challenges balancing the risk of toxicities with benefits of treatment. Over one-third of the respondents reported skill gaps when selecting suitable treatment options and prescribing therapies and reported a lack in confidence to initiate and manage treatment. Larger gaps in knowledge of guidelines and skills in patient assessment were identified in MCL, compared to CLL. CONCLUSIONS: This study suggests the need for continuing medical education specifically to improve knowledge of treatment guidelines, and to assist clinicians in developing skills and confidence when faced with clinical decision-making scenarios of patients with specific comorbidities and/or molecular test results, for example, through case-based learning activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".