Correlation of the Transfusion Camp knowledge assessment test with clinical transfusion practice
Bibliographic record
Abstract
BACKGROUND: It is uncertain how transfusion knowledge translates to practice. The purpose of the study was to determine if higher scores on a validated Transfusion Camp knowledge assessment test were associated with transfusion order appropriateness. STUDY DESIGN AND METHODS: Eligible participants included postgraduate trainees and faculty physicians who had prescribed at least four transfusion orders in the preceding 6 months at two hospitals. Participant data and knowledge were collected using a web-based questionnaire with a validated Transfusion Camp knowledge assessment tool. The most recent 4-10 consecutive transfusion orders per prescriber were independently dually adjudicated for appropriateness based on published criteria. The primary outcome was the correlation between the score on six questions on red blood cells (RBCs), platelets (PLTs), and plasma from the validated test and the percentage order appropriateness. Generalized linear regression was conducted to determine if factors (sex, specialty, participation in Transfusion Camp, previous transfusion education, self-rated knowledge) were associated with appropriate orders. RESULTS: Seventy-four participants (45 trainees, 29 faculty; 31 females, 43 males) completed the test. Median score was 66.7% (interquartile range [IQR]: 50.0, 83.3) for six questions on RBCs, PLTs, and plasma transfusions. Of 546 transfusion orders adjudicated, appropriateness was 90.7% (95% confidence interval [CI]: 87.9%-93.0%). The correlation between prescriber test scores and order appropriateness was very weak (r = -.08). In multivariable analysis, female prescribers (p = .02) and beginner (vs. intermediate) self-rated knowledge (p = .01) were associated with higher transfusion appropriateness. CONCLUSION: Transfusion knowledge test scores did not correlate with order appropriateness. Factors other than knowledge are key to understanding how to improve appropriate blood use.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".