Comprehensive Simulation Curriculum of Transfusion Medicine
Bibliographic record
Abstract
Abstract Introduction This resource is a comprehensive curriculum of transfusion medicine with emphasis on acute hemolytic transfusion medicine and massive transfusion protocol. It includes scenarios located in various hospital settings (i.e., ward, operating room, ICU) to allow for training of variety of learners. The simulation scenario, suggested reading, PowerPoint presentations and test allow for learners to gain transfusion medicine knowledge, skills, and attitudes crucial for anyone who uses blood products in their health care practice. Methods The included material presents four simulation scenarios, which allow participants to manage a massively bleeding patient and/or a patient with acute hemolytic transfusion reaction in different clinical environments. The scenarios are based on the following clinical situations: (1) an obstetric patient undergoing cesarean section complicated with a massive bleeding in the operating room, (2) a postsurgery patient who underwent liver transplant with massive bleeding from drains in the ICU, (3) a floor patient with massive gastrointestinal bleeding, and (4) an acute hemolytic transfusion reaction scenario in an awake patient on the floor. Results At our institution we have utilized the transfusion curriculum for 1 year in education of medical students, anesthesiology residents, and surgery residents. We had 95 learners participate in this educational activity. On a scale from 0 to 5 (5 being exceptional, 1 being unsatisfactory) average learner satisfaction was rated as 4.43 +/− 0.5 (range 3 to 5). Learner knowledge increased from pretest to posttest with pretest scores averaging 39% and posttests averaging 77.5%. Knowledge was also retained 6 weeks later at 82% correctly answered questions. Discussion The authors feel strongly that this educational curriculum is an excellent educational tool. Through the design process we have learned about interdisciplinary development of curriculums, learner satisfaction, educational effectiveness of the curriculum, and learner gaps that need attention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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".