Guidance for Prenatal, Postnatal and Neonatal Immunohematology Testing in Canada: Consensus Recommendations from a Modified Delphi Process
Bibliographic record
Abstract
OBJECTIVES: Blood Group, antibody screen, fetal maternal hemorrhage tests and Rh(D) immunoglobulin (RhIG) administration are interventions during pregnancy that aid in the prevention of hemolytic disease of the fetus and newborn (HDFN). The timing, frequency, and nature of testing vary across centres due to limited data to inform standards development. Using Delphi methodology, this study aimed to establish guidance for Canadian practice related to prenatal, postnatal and neonatal immunohematologic testing, and RhIG administration, to reduce risk and improve diagnosis of HDFN. METHODS: A national, multidisciplinary Delphi panel rated their agreement with potential guidance statements related to prenatal, postnatal and neonatal immunohematology testing on a 5-point Likert scale during iterative rounds of voting. After each round, responses were analyzed and statements were re-sent to the panel for further ratings until consensus was achieved, defined as Cronbach's α >0.95 or a maximum of 3 voting rounds. At the conclusion of the Delphi process, statements rated ≥4/5 were included. RESULTS: In total, 46 experts voted on 49 proposed statements. Consensus was achieved after 3 survey rounds (Cronbach's α = 0.94), with a 100% response rate throughout. Overall, 44 statements reached consensus. Statements focused on prenatal immunohematology testing (N = 21 statements), maternal-fetal hemorrhage testing and RhIG administration during pregnancy (N = 15), and testing of neonates for surveillance of hyperbilirubinemia secondary to hemolytic disease of the newborn (N = 8). CONCLUSIONS: This Canadian consensus guidance aims to optimize the surveillance of pregnancies at risk of HDFN and the dosing and timing of RhIG administration. It provides actionable recommendations to harmonize practice and support safe, timely, and cost-effective care.
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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 | 0.188 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".