Laboratory assessment of fetomaternal haemorrhage and Rh immune globulin management: Canadian practice and scoping review
Bibliographic record
Abstract
Fetomaternal haemorrhage (FMH) in RhD-negative individuals can lead to alloimmunization with future antibody-mediated destruction of fetal red blood cells. Accurate estimation of FMH is essential for guiding the dose of Rh immune globulin and mitigating alloimmunization. We conducted a national survey and performed a scoping review to determine the availability, technical characteristics and clinical limitations of FMH tests. We describe the evolution of FMH testing, including the qualitative methods (alpha-fetoprotein level, microscopic weak D test, enzyme-linked antiglobulin test, rosette test, gel agglutination cards) and the quantitative methods (Kleihauer-Betke test, flow cytometry assays). Although the rosette test is the most commonly used qualitative method, it may yield false-negative results with fetal RhD variants or false-positives with maternal RhD variants or a positive direct antiglobulin test. Kleihauer-Betke test is the most commonly used quantitative test but has limitations: It is labour-intensive, prone to interobserver variability and can overestimate FMH when maternal F-cell levels are elevated (e.g. haemoglobinopathies). While modifications to the Kleihauer-Betke test can enhance accuracy, flow cytometry remains the most accurate quantification method. With limitations in flow cytometry availability, efficient sample prioritization and pragmatic referral protocols are required. We propose an algorithm for FMH test selection to support decision-making across clinical contexts and resource settings.
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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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.032 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".