Predictors of Fetal and Maternal Outcome in the Crucible of Hepatic Dysfunction During Pregnancy
Bibliographic record
Abstract
BACKGROUND: Hepatic dysfunction during pregnancy places both the mother and the fetus at risk. Investigations which are efficient, cost effective and easily available for prognostication are required to tackle this global problem. We studied the etiologies and evaluated investigations for predictive efficiency. METHODS: One hundred ninety-seven pregnant women with hepatic dysfunction during pregnancy were identified. All patients were followed up till 8 weeks after termination of pregnancy or death. Clinico-demographic, biochemical and hematological data were collected and analyzed. RESULTS: One hundred ninety-seven of 6,122 females had abnormal liver function tests. Pre-eclampsia (57%), eclampsia (19%), HELLP syndrome (8%), viral infection (6%), hyperemesis gravidarum (5%), intrahepatic cholestasis of pregnancy (4%), chronic liver disease (1%) and sepsis were encountered. There were 41 fetal deaths, 42% preterm deliveries, and NICU admission rate was 27%. Five maternal deaths occurred. Maternal anemia, thrombocytopenia, hyperbilirubinemia and coagulopathy were statistically significant in adverse fetal outcomes. Serum bilirubin performed better than INR as a predictor of both maternal and fetal outcomes. CONCLUSIONS: Hepatic dysfunction during pregnancy is associated with adverse events for both the mother and the fetus and hypertensive disorders remain the major cause. Maternal bilirubin levels and INR have a role in predicting adverse feto-maternal outcome.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".