Interpretation of pretransfusion testing in obstetrical patients who have received antepartum Rh immunoglobulin prophylaxis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Determining whether anti-D represents active or passive alloimmunization after RhIg administration is challenging. The objectives were to use antibody reaction strength to differentiate patients who may have become RhD alloimmunized during pregnancy from those manifesting passive anti-D and to investigate which methods work best for this determination. MATERIALS AND METHODS: Data were collected from patients residing in the Edmonton region of Canada, ≥18 years old, undergoing antibody screening in late pregnancy, who received 300 μg (1500 IU) of RhIg in the preceding 120 days. A total of 1106 tests were performed on 1050 blood samples from 963 patients: 640 by PEG, 156 by gel-card and 310 by solid-phase methodology. RESULTS: PEG was the least sensitive to passive anti-D, with significantly fewer positive results at ≥8 weeks after RhIg compared to the other methods. Strength of reactivity and time since RhIg injection could be used to identify patients at high risk using PEG as a 4+ reaction at any time, ≥3+ at >2 weeks, ≥2+ at >6 weeks and ≥1+ at >14 weeks. Similarly, the gel-card method thresholds were 4+ at >5 weeks, ≥3+ at >10 weeks and ≥2+ at >15 weeks. Reaction strength by solid-phase was too variable to establish useful thresholds by this method. Infant RhD status did not significantly affect results. CONCLUSION: Patients can be risk stratified for alloimmunization by anti-D reaction strength and time after RhIg administration. The PEG method was the best of those investigated, but the gel-card method can also be used.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".