Internal medicine resident knowledge of transfusion medicine: results from the <scp>BEST</scp> ‐ <scp>TEST</scp> international education needs assessment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Blood transfusion is the most common hospital procedure performed in the United States. While inadequate physician transfusion medicine knowledge may lead to inappropriate practice, such an educational deficit has not been investigated on an international scale using a validated assessment tool. Identifying specific deficiencies is critical for developing curricula to improve patient care. STUDY DESIGN AND METHODS: Rasch analysis, a method used in high-stakes testing, was used to validate an assessment tool consisting of a 23-question survey and a 20-question examination. The assessment tool was administered to internal medicine residents to determine prior training, attitudes, perceived ability, and actual knowledge related to transfusion medicine. RESULTS: A total of 474 residents at 23 programs in nine countries completed the examination. The overall mean score of correct responses was 45.7% (site range, 32%-56%). The mean score for Postgraduate Year (PGY)1 (43.9%) was significantly lower than for PGY3 (47.1%) and PGY4 (50.6%) residents. Although 89% of residents had participated in obtaining informed consent from a patient for transfusion, residents scored poorly (<25% correct) on questions related to transfusion reactions. The majority of residents (65%) would find additional transfusion medicine training "very" or "extremely" helpful. CONCLUSION: Internationally, internal medicine residents have poor transfusion medicine knowledge and would welcome additional training. The especially limited knowledge of transfusion reactions suggests an initial area for focused training. This study not only represents the largest international assessment of transfusion medicine knowledge, but also serves as a model for rigorous, collaborative research in medical education.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it