Severe Gestational Thrombocytopenia: A Case Report and Brief Review of the Literature
Bibliographic record
Abstract
Thrombocytopenia (TCP) is a common medical finding in obstetric population at term. The majority of new-onset TCP cases are mild, asymptomatic and diagnosed accidentally on routine antenatal screening. The most common causes at term are gestational thrombocytopenia (GT), preeclampsia/HELLP syndrome and immune-mediated thrombocytopenia (ITP). Preeclampsia/HELLP syndrome is accompanied with well-defined clinical characteristics and specific laboratory findings, while the other two are usually asymptomatic and are impossible to distinguish from one another. We encountered a case of new-onset TCP at 40 weeks gestation with negative history and a platelet count of 33 × 10 9 /L, yet, who had a fast spontaneous postpartum recovery. Her second pregnancy was also complicated by TCP of 77 × 10 9 /L at 37 weeks gestation. The newborn platelet count was normal in both instances. She was considered to have GT after a lapse of 4 years, being consistently healthy with normal platelet counts. After excluding other serious causes of severe new-onset TCP at term, management should be oriented towards securing hemostasis in preparation for delivery without wasting precious time and resources trying to discern between GT and ITP. J Hematol. 2016;5(4):142-150 doi: https://doi.org/10.14740/jh308w
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".