Understanding Thrombocytopenia in the Obstetric Population: A Study from a Tertiary Care Center
Bibliographic record
Abstract
Background: Thrombocytopenia in pregnancy is a common condition with diverse etiologies, ranging from benign causes such as gestational thrombocytopenia (GT) to more serious conditions like preeclampsia and immune thrombocytopenic purpura (ITP). The clinical implications of thrombocytopenia during pregnancy include potential maternal and fetal complications, highlighting the importance of early detection and appropriate management. Objective: To evaluate the incidence, causes, clinical outcomes, and complications of thrombocytopenia in pregnancy at a tertiary care hospital. Methods: This retrospective cohort study included 130 pregnant women who were diagnosed with thrombocytopenia during their antenatal care between 2020 and 2021. Data on demographics, etiology, severity of thrombocytopenia, and maternal and fetal outcomes were collected and analyzed. Results: The incidence of thrombocytopenia in pregnancy was found to be 3.85%. The most common causes were gestational thrombocytopenia (48.48%), preeclampsia (18.18%), and anemia (27.27%). Mild thrombocytopenia (<100,000/µL) was the most frequent severity (68.18%), with severe thrombocytopenia (<50,000/µL) observed in 6.06% of cases. Maternal complications included postpartum hemorrhage (10.60%) and incision site oozing (7.57%). Fetal outcomes included intrauterine growth restriction (12.12%) and birth asphyxia (7.57%). Most cases were diagnosed in the second trimester, and a significant proportion (56.06%) were in primigravida women. Conclusion: Thrombocytopenia in pregnancy is predominantly mild, with gestational thrombocytopenia being the most common cause. Although the condition generally carries a good prognosis, associated complications such as postpartum hemorrhage and adverse fetal outcomes underscore the need for careful monitoring. Early diagnosis and individualized management are essential to minimize risks for both mother and child.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".