Planning antepartum directed donations in preparation for neonatal cardiac surgery in the absence of compatible donors
Bibliographic record
Abstract
BACKGROUND: haplotype, characterized by the absence of the high frequency antigen Sec, as well as partial C and e antigens, is rare and is associated with potential for alloimmunization. Anti-Sec has been reported to be associated with a risk of delayed hemolytic transfusion reaction and hemolytic disease of the fetus and newborn (HDFN). RESULTS: haplotype with anti-Sec, anti-c, and anti-e. Morphological ultrasound identified dextro-transposition of the great arteries in the fetus. Neonatal cardiac surgery was planned with cardiopulmonary bypass support. Due to the rarity of this genotype, there were no compatible donors in our registry. For this reason, in addition to two previously glycerolized maternal donations, the mother donated three units during pregnancy and one unit postpartum. DISCUSSION: This case highlights the many complexities for the blood supplier pertaining to organizing blood donations during pregnancy, the risk of leukoreduction failure and jellification during the deglycerolization process of units from donors with rare blood carrying the SCT, as well as planning the rare-blood inventory and managing expiry dates to provide transfusion support during delivery, neonatal surgery, and the postoperative period. This case also exemplifies the importance of a strong partnership between blood suppliers and medical teams.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".