Neonatal alloimmune thrombocytopenia due to anti-HPA-2b (anti-Ko <sup>a</sup> )
Bibliographic record
Abstract
Most severe cases of neonatal alloimmune thrombocytopenia (NAIT) are due to anti-HPA-1a (anti-PlA1) antibodies. We report a case of NAIT due to anti-HPA-2b that resulted in in utero intracranial hemorrhage.A 33-year-old G2P1A0 Caucasian woman had a routine ultrasound at 34 weeks. The fetus appeared to have a left hemispheric hematoma. IVIG, 1g/kg, was started immediately and administered weekly until delivery. One day after receiving the first dose of IVIG, fetal platelet count was 18 x 10(9)/L, and Hb was 116 g/L. Eleven mL of matched platelets compatible by monoclonal antibody immobilization of platelet antigens (MAIPA) assay were transfused in utero, raising the platelet count to 62 x 10(9)/L. Repeat transfusions were done later that week and 1 week later, with pretransfusion counts of 19 x 10(9)/L and 16 x 10(9)/L, respectively. Delivery by C section was done at 35.5 weeks, after the third platelet transfusion. Platelet count at birth was 77 x 10(9)/L. Drainage of the hematoma was performed after transfusion. Testing with a solid phase ELISA revealed reactivity against GP1b/IX. MAIPA testing after platelet treatment with the protease inhibitor leupeptin demonstrated the presence of anti-HPA-2b. On PCR-SSP the mother was HPA-2a homozygous, the father was HPA-2a/2b. Antibodies against the HPA-2b antigen located on the GP1b/IX complex have been reported in rare cases of NAIT. Testing is complicated by proteolytic degradation of the antigen-bearing fragment. Compatible platelets are easily found since approximately 85 percent of donors are HPA-2a/2a.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".