Current state of undergraduate medical school training in transfusion medicine and its impact on postgraduate trainee knowledge
Bibliographic record
Abstract
BACKGROUND: Studies have described poor transfusion medicine (TM) knowledge in postgraduate trainees. The impact of undergraduate medical TM education on postgraduate knowledge is unclear. METHODS: Canadian medical schools were surveyed on the number of hours dedicated to TM teaching and topics covered by curricula during 2016-2020. Postgraduate trainees attending Transfusion Camp in 2021 completed a pretest of 20 multiple-choice questions. The survey results and pretest scores were compared to evaluate the association between undergraduate medical TM education and pretest scores. RESULTS: The survey was completed by 16 of 17 Canadian medical schools. The number of hours (h) of TM teaching were <2 h (25%), 3-4 h (25%), and >4 h (50%). Twelve of 19 Transfusion Camp topics were covered in ≥50% of schools. Eleven medical schools provided ethics approvals/waivers to include trainee pretest scores in the analysis (N = 200). The median pretest scores by medical school ranged from 48% to 70%. No association was found between number of TM teaching hours and average pretest scores (p = .60). There was an association between higher postgraduate year level and individual pretest score (p < .0001). The analysis by topic demonstrated questions where trainees from different schools performed uniformly well or poorly; other topics showed considerable variation. CONCLUSION: Variation in quantity and content of undergraduate TM teaching exists across Canadian medical schools. In this limited assessment, the number of TM teaching hours was not associated with performance on the pretest. This study raises the opportunity to re-evaluate the delivery (content, timing, consistency) of TM education in undergraduate medical schools.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".