A practical approach to immune thrombocytopenia in pregnancy
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) often presents for the first time in pregnancy, or, in patients with a history of ITP, pregnancy can trigger a relapse. ITP in pregnancy is often mild, leading to minimal or no symptoms; however, treatment may be needed if thrombocytopenia becomes severe, if bleeding occurs, or in anticipation of delivery and neuraxial analgesia. To facilitate the diagnosis of ITP in pregnancy, we present a systematic approach that allows clinicians to first consider urgent pregnancy-related thrombocytopenic conditions such as hypertensive disorders of pregnancy or thrombotic thrombocytopenic purpura; exclude other causes of thrombocytopenia; and determine the need for treatment. We review options for first-line therapies for ITP in pregnancy, including corticosteroids (prednisone or methylprednisolone) and intravenous immune globulin, which has a favorable safety profile in pregnancy, and second-line therapy options that have been used in pregnancy including thrombopoietin receptor agonists, rituximab, and certain immunosuppressant medications such as azathioprine. We summarize the recommendations for platelet targets for delivery, recognizing that the evidence is limited, including a platelet count of 50 × 109/L or higher for caesarean delivery and 70 × 109/L or higher for neuraxial anesthesia. Treatment decisions for ITP in pregnancy should be informed by patients' values and preferences along with a multidisciplinary team that includes hematologists, obstetricians, and anesthesiologists.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".