Simulation‐based education for transfusion medicine
Bibliographic record
Abstract
BACKGROUND: The administration of blood products is frequently determined by physicians without subspecialty training in transfusion medicine (TM). Education in TM is necessary for appropriate utilization of resources and maintaining patient safety. Our institution developed an efficient simulation-based TM course with the goal of identifying key topics that could be individualized to learners of all levels in various environments while also allowing for practice in an environment where the patient is not placed at risk. STUDY DESIGN AND METHODS: A 2.5-hour simulation-based educational activity was designed and taught to undergraduate medical students rotating through anesthesiology and TM elective rotations and to all Clinical Anesthesia Year 1 (CA-1) residents. Content and process evaluation of the activity consisted of multiple-choice tests and course evaluations. RESULTS: Seventy medical students and seven CA-1 residents were enrolled in the course. There was no significant difference on pretest results between medical students and CA-1 residents. The posttest results for both medical students and CA-1 residents were significantly higher than pretest results. The results of the posttest between medical students and CA-1 residents were not significantly different. CONCLUSION: The TM knowledge gap is not a trivial problem as transfusion of blood products is associated with significant risks. Innovative educational techniques are needed to address the ongoing challenges with knowledge acquisition and retention in already full curricula. Our institution developed a feasible and effective way to integrate TM into the curriculum. Educational activities, such as this, might be a way to improve the safety of transfusions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".