Bibliographic record
Abstract
Transfusion knowledge and experience is essential for all haematologists, and this is reflected in the haematology training curriculum and examinations.Incidents reported to SHOT repeatedly highlight the importance of transfusion training for patient safety.Previous national surveys of transfusion training in 2008 and 2012 found widespread dissatisfaction and inconsistencies in delivery of training.This survey was carried out to understand the current picture of transfusion training across the United Kingdom.A 30-question online survey was distributed to haematology specialty trainees in June-July 2023.150 responses (response rate 24%) were received from UK trainees at ST3-7 (England 82.7%, Northern Ireland 6.0%, Scotland 6.0%, Wales 5.3%; ST3 8.7%, ST4 22.2%, ST5 22.0%, ST6 29.3%, ST7 17.3%).48 (56.0%) trainees had undertaken a dedicated transfusion post, in 13 deaneries.The posts undertaken were at Blood Services (31.3%),hospitals (33.3%) or both (35.4%).Deficiencies in laboratory aspects of transfusion training were highlighted frequently.The most common barrier to training identified by trainees was lack of exposure to the transfusion laboratory (75%), followed by other clinical
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.817 | 0.609 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".