Rh(D) immune globulin administration in pregnancy: Retrospective audit of patient safety events followed by targeted educational intervention with Bayesian analysis
Bibliographic record
Abstract
OBJECTIVES: To examine local patient safety events related to the administration of anti-Rh(D) immune globin (RhIG) during pregnancy, and to follow-up with targeted educational intervention to improve knowledge of this process. BACKGROUND: Administration RhIG is established treatment for the prevention of haemolytic disease of the foetus and newborn (HDFN). However, patient safety events in relation to its correct use continue to occur. METHODS: A retrospective audit of patient safety events related to RhIG administration during pregnancy was performed. Targeted educational intervention in the form of PowerPoint® presentation were given to nursing staff, laboratory staff and physicians and evaluated with pre- and post-tests using multiple-choice questions given immediately before and after the presentation. RESULTS: An annual incidence of 0.24% of patient safety events related to the administration of RhIG during pregnancy was found. These events were mostly in the preanalytical phase, for example mislabelled samples or samples for D-rosette/Kleihauer-Betke testing drawn from the baby, not the mother. Using Bayesian analysis, the probability of positive effect for the targeted educational intervention was 100% with a median improved score of 29%. This was compared with a control group using standard curriculum education intervention based on the current curriculum for nursing, laboratory and medical students which showed a median improved score of only 4.4%. CONCLUSIONS: Administration of RhIG during pregnancy is a multistep process involving health care professionals of several disciplines providing opportunities to enhance the curriculum for nursing, laboratory and medical students and to ensure on-going education.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".