Evaluation of immunohematology knowledge in hematology trainees
Bibliographic record
Abstract
BACKGROUND: Canadian hematology trainees are expected to attain clinical knowledge in the subject of red blood cell and platelet antigen systems and the principles of transfusion medicine. However, the relative degree of expertise required in blood bank serology is not well defined. STUDY DESIGN AND METHODS: A modified Delphi approach involving 10 Canadian hematology program directors was utilized to identify 12 relevant topics in immunohematology. A multiple-choice exam was developed and validated among hematology trainees from 13 hematology training programs across Canada. A Rasch analysis was used to determine fit of the examination before deploying the exam the following year to ascertain the level of knowledge in hematology trainees. RESULTS: The exam was piloted with 62 hematology trainees. The reliability of the exam was 0.93 with a mean item fit score of 1.01. The exam was able to discriminate between training years and self-rated expertise with better performance attained by more advanced trainees (p < 0.01). No differences were seen between geographic regions. A modified version of the exam was deployed the following year to 85 trainees, with a mean score of 58.9% ± 15.3%. Trainees scored poorest on topics concerning antibody investigations and D variants. CONCLUSION: A standardized exam for assessing hematology trainees on their expected expertise in transfusion immunohematology has been developed and can be used to assess the efficacy of educational resources provided in the subject. Trainees had a low overall mean score indicating additional educational initiatives are warranted.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".