Management and outcomes of women with low fibrinogen concentration during pregnancy or immediately postpartum: A UK national population‐based cohort study
Bibliographic record
Abstract
INTRODUCTION: Pregnant women with a fibrinogen level <2 g/L represent a high-risk group that is associated with severe postpartum hemorrhage and other complications. Women who would qualify for fibrinogen therapy are not yet identified. MATERIAL AND METHODS: A population-based cross-sectional study was conducted using the UK Obstetric Surveillance System between November 2017 and October 2018 in any UK hospital with a consultant-led maternity unit. Any woman pregnant or immediately postpartum with a fibrinogen <2 g/L was included. Our aims were to determine the incidence of fibrinogen <2 g/L in pregnancy, and to describe its causes, management and outcomes. RESULTS: Over the study period 124 women with fibrinogen <2 g/L were identified (1.7 per 10 000 maternities; 95% confidence interval 1.4-2.0 per 10 000 maternities). Less than 5% of cases of low fibrinogen were due to preexisting inherited dysfibrinogenemia or hypofibrinogenemia. Sixty percent of cases were due to postpartum hemorrhage caused by placental abruption, atony, or trauma. Amniotic fluid embolism and placental causes other than abruption (previa, accreta, retention) were associated with the highest estimated blood loss (median 4400 mL) and lowest levels of fibrinogen. Mortality was high with two maternal deaths due to massive postpartum hemorrhage, 27 stillbirths, and two neonatal deaths. CONCLUSIONS: Fibrinogen <2 g/L often, but not exclusively, affected women with postpartum hemorrhage due to placental abruption, atony, or trauma. Other more rare and catastrophic obstetrical events such as amniotic fluid embolism and placenta accreta also led to low levels of fibrinogen. Maternal and perinatal mortality was extremely high in our cohort.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".