Transfusion Camp: The <scp>UK</scp> experience and its value in improving knowledge of transfusion medicine among postgraduate trainees
Bibliographic record
Abstract
OBJECTIVES: To report the UK experience of rolling out Transfusion Camp. BACKGROUND: Transfusion Camp is a structured education programme developed in Toronto, with the aim of reducing knowledge gaps in transfusion medicine in postgraduate trainees. It consists of didactic lectures viewed online by the participants, then interactive, locally delivered seminars. Since 2015, it has been rolled out in the United Kingdom, and is now available in four centres. Here, we report the UK experience of Transfusion Camp and outcomes. METHODS: Trainees are recruited via the training programme directors in each region. Pre- and post-course assessments are administered using the validated BEST (Biomedical Excellence for Safer Transfusion) test, with possible scores 0-20, and confidence measured on an A-E Likert scale. RESULTS: Since 2015, 130 trainees have participated in Transfusion Camp in the United Kingdom. Trainees from all specialties significantly improved their BEST-test scores after attending the course (mean score 11.6/20 before the course, compared with 14.3/20 after the course), and confidence in managing transfusion-related issues was also significantly improved. CONCLUSION: We recommend that all centres consider offering Transfusion Camp to trainees in haematology and other specialties that frequently use blood transfusions, such as anaesthesia/ICU, Internal Medicine and others.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".