BEST‐TEST2: assessment of hematology trainee knowledge of transfusion medicine
Bibliographic record
Abstract
BACKGROUND: As transfusion is a common therapy and key component in every hematologist's practice, hematology training programs should dedicate significant time and effort to delivering high-quality transfusion medicine education to their trainees. The current state of hematology trainee knowledge of transfusion medicine is not known. STUDY DESIGN AND METHODS: A validated assessment tool developed by the Biomedical Excellence for Safer Transfusion (BEST) Collaborative was used to assess prior transfusion medicine education, attitudes, perceived ability, and transfusion medicine knowledge of hematology trainees. RESULTS: A total of 149 hematology trainees at 17 international sites were assessed. The overall mean exam score was 61.6% (standard deviation, 13.4%; range, 30%-100%) with no correlation in exam scores with postgraduate year or previous transfusion medicine education in medical school or internal medicine residency. However, better scores correlated with 3 or more hours of transfusion medicine education (p = 0.0003) and perceived higher-quality education during hematology training (p = 0.03). Hematology trainees at US sites, where hematology is often combined with oncology training, had statistically lower scores than trainees at non-US sites (56.2% vs. 67.4%; p < 0.0001). In terms of topic areas, although 93% of participants had obtained consent for transfusion, the lowest scores were on transfusion reaction-related questions. CONCLUSION: Given the overall poor performance, this study serves as an impetus for all hematology training programs to reevaluate the quality and quantity of transfusion medicine training and can assist in the development of targeted curricula.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".