Internal medicine resident knowledge of transfusion medicine: results from the <scp>BEST</scp> ‐ <scp>TEST</scp> international education needs assessment
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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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".