Blood transfusion knowledge of surgical residents: is an educational intervention effective?
Bibliographic record
Abstract
BACKGROUND: Evidence-based transfusion education for surgical residents is crucial to improving practice. A pilot study was undertaken to assess the effectiveness of an education module for improving transfusion knowledge among surgical residents. METHODS: Modules were developed and delivered by experts in surgery and transfusion medicine. They were delivered to residents in their first 2 years of training (Surgical Foundations), and to General Surgery residents across all years of training. Premodule and postmodule and retention knowledge assessments were used to assess efficacy. Median assessment scores for each group were compared using a two-sample Wilcoxon rank-sum analysis. Chi-square tests were used to compare each group's correct response rates for each question across the three tests. RESULTS: Median assessment scores of residents in the Surgical Foundations program improved from a mean of 60% premodule to 80% postmodule and remained at 80% in the retention assessment (p < 0.01). Improvements were seen in the areas of transfusion dose, preoperative blood management, management of reactions, and informed consent (p < 0.01). Assessment scores of General Surgery residents also improved from 59%, to 73%, and to 82%, with significant improvement (p < 0.01) between premodule and postmodule scores. Improvements in correct response rates were seen in the areas of management of anemia and permissive hypotension in trauma (p < 0.01). CONCLUSIONS: Transfusion knowledge of surgical residents was improved by a collaborative educational initiative. This could serve as a model for other training programs to improve resident knowledge of evidence-based transfusion practices. The efficacy of such interventions in changing practice remains untested.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".