Transfusion Testing During Routine Pregnancies: Consensus Recommendations from a Modified Delphi Process
Bibliographic record
Abstract
Objective To standardize perinatal transfusion testing and Rh immunoglobulin (RhIG) administration in low-risk pregnancies through the creation of expert consensus statements. Methods Modified Delphi consensus process involving iterative rounds of voting on statements by a national expert panel. After each round, responses were analyzed and resent to the panel for further ratings until consensus was achieved, defined as Cronbach alpha >0.95 or a maximum of 3 voting rounds. Once consensus was achieved, statements with a median score ≥4 out of 5 were included. Results Forty-six expert panelists participated with representation across Canadian provinces and care providers including maternal fetal medicine, obstetrics, family practice, transfusion medicine, neonatology, midwifery, nursing and a patient representative. Twenty-one statements related to perinatal transfusion testing and RhIG administration met criteria for inclusion in the final set of statements. The two statements with the lowest proportion of "strongly agree" votes pertained to eliminating the 28-week group and screen in those with a negative first trimester screens and eliminating the need for RhIG before 12 weeks in threatened, spontaneous or therapeutic abortions. Conclusion These 21 expert consensus statements aim to harmonize perinatal practice across Canada addressing conflicting guidelines and resource limitations, especially in rural settings. This is the first set of expert consensus statements that captures the Canadian context with coverage from first trimester to the birth of the neonate. These statements follow Choosing Wisely principles. Some are practice changing and will require efforts to ensure implementation into practice.
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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 | 0.320 | 0.280 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".