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Record W4417042143 · doi:10.1182/hematology.2025000743

A practical approach to immune thrombocytopenia in pregnancy

2025· article· en· W4417042143 on OpenAlexaff
Kristine Matusiak, Ann Kinga Malinowski

Bibliographic record

VenueHematology · 2025
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsMcMaster UniversityImpact
Fundersnot available
KeywordsPregnancyRomiplostimThrombopoietinEltrombopagImmune thrombocytopeniaImmune systemThrombopoietin receptorMultidisciplinary team

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.608
Threshold uncertainty score0.303

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.350
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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