DIC in Pregnancy – Pathophysiology, Clinical Characteristics, Diagnostic Scores, and Treatments
Bibliographic record
Abstract
Obstetrical hemorrhage and especially DIC (disseminated intravascular coagulation) is a leading cause for maternal mortality across the globe, often secondary to underlying maternal and/or fetal complications including placental abruption, amniotic fluid embolism, HELLP syndrome (hemolysis, elevated liver enzymes and low platelets), retained stillbirth and acute fatty liver of pregnancy. Various obstetrical disorders can present with DIC as a complication; thus, increased awareness is key to diagnosing the condition. DIC patients can present to clinicians who may not be experienced in a variety of aspects of thrombosis and hemostasis. Hence, DIC diagnosis is often only entertained when the patient already developed uncontrollable bleeding or multi-organ failure, all of which represent unsalvageable scenarios. Beyond the clinical presentations, the main issue with DIC diagnosis is in relation to coagulation test abnormalities. It is widely believed that in DIC, patients will have prolonged prothrombin time (PT) and partial thromboplastin time (PTT), thrombocytopenia, low fibrinogen, and raised D-dimers. Diagnosis of DIC can be elusive during pregnancy and requires vigilance and knowledge of the physiologic changes during pregnancy. It can be facilitated by using a pregnancy specific DIC score including three components: 1) fibrinogen concentrations; 2) the PT difference - relating to the difference in PT result between the patient's plasma and the laboratory control; and 3) platelet count. At a cutoff of ≥26 points, the pregnancy specific DIC score has 88% sensitivity, 96% specificity, a positive likelihood ratio (LR) of 22, and a negative LR of 0.125. Management of DIC during pregnancy requires a prompt attention to the underlying condition leading to this complication, including the delivery of the patient, and correction of the hemostatic problem that can be guided by point of care testing adjusted for pregnancy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".