The use of an objective structured clinical examination to assess internal medicine residents' transfusion knowledge
Bibliographic record
Abstract
BACKGROUND: Previous studies have indicated that medical trainees receive inadequate instruction in transfusion medicine and that this translates into insufficient transfusion knowledge throughout medical practice. We undertook to assess transfusion knowledge among internal medicine trainees in a single institution using a standardized oral examination case. STUDY DESIGN AND METHODS: A case of an adult with clear requirement for red blood cell transfusion was created. The case was reviewed by content experts and pilot tested on hematology trainees in their fifth postgraduate training year. The case was administered to internal medicine trainees in their first to fourth postgraduate years as part of a larger examination that is mandatory for all internal medicine trainees in our center. Scores on the transfusion case were assessed for each examinee. RESULTS: Seventy-three residents participated in the examination and completed the transfusion objective structured clinical examination (OSCE) case. The mean score on this case was 6.6 of 10 with 31.5% of examinees failing this case. Scores did not differ as a function of increasing years of training. CONCLUSION: This study again indicated that transfusion knowledge is poor among internal medicine trainees and that this does not improve with increasing number of years of training. Innovative strategies for transfusion education are urgently needed and should be rigorously assessed for efficacy.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".