A retrospective 11-year analysis of obstetric patients with idiopathic thrombocytopenic purpura
Bibliographic record
Abstract
Numerous studies have examined the outcomes of infants born to mothers with idiopathic thrombocytopenic purpura (ITP). Fewer studies have discussed the morbidity of obstetric patients with ITP. We describe a retrospective study of 92 women with ITP during 119 pregnancies over an 11-year period. Most women had thrombocytopenia during pregnancy. At delivery, women in 98 pregnancies (89%) had platelet counts lower than 150 x 109/L; most had mild to moderate thrombocytopenia. For many, the pregnancy was uneventful; however, women had moderate to severe bleeding in 25 pregnancies (21.5%). Women in 37 pregnancies (31.1%) required treatment to increase platelet counts. During delivery, 44 women (37.3%) received epidural analgesia without complications, with most having a platelet count between 50 and 149 x 109/L. Most deliveries (82.4%) were vaginal. Bleeding was uncommon at delivery. Infant platelet counts at birth ranged from 12 to 436 x 109/L; 25.2% of infants had platelet counts lower than 150 x 109/L, and 9% had platelet counts lower than 50 x 109/L. Eighteen infants (14.6%) required treatment for hemostatic impairment. Two fetal deaths occurred. One was caused by hemorrhage. ITP in pregnancy carries a low risk, but mothers and infants may require therapy to raise their platelet counts.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".