Amniotic fluid embolism: incidence, risk factors, and impact on perinatal outcome
Bibliographic record
Abstract
OBJECTIVE: To extend our previous work on AFE in Canada by including stricter criteria for case identification and by examining risks for stillbirth, neonatal mortality and serious maternal and neonatal morbidity. DESIGN: Population-based cohort study. SETTING: Canada. POPULATION OR SAMPLE: In all, 4,508,462 hospital deliveries from fiscal year 1991/92 to 2008/09. METHODS: To reduce false-positive diagnoses, we restricted our analysis to AFE cases with cardiac arrest, shock or severe hypertension, respiratory distress, mechanical ventilation, coma, seizure, or coagulation disorder. Linkage of maternal and neonatal records, available since 2001/02, enabled us to examine the effects of AFE on neonatal outcomes. Detailed demographic and clinical data facilitated control for a broad array of potential confounding variables. MAIN OUTCOME MEASURES: Amniotic fluid embolism, in-hospital neonatal death, asphyxia, mechanical ventilation, bacterial sepsis, seizure, nonimmune haemolytic or traumatic jaundice and length of hospital stay. RESULTS: A total of 292 AFE cases were identified, of which only 120 (40%) were confirmed after applying our additional diagnostic criteria, yielding an AFE incidence of 2.5 per 100,000 deliveries. Of the 120 confirmed cases, 33 (27%) were fatal. Significant modifiable risk factors included medical induction, caesarean delivery, instrumental vaginal delivery, and uterine or cervical trauma. Amniotic fluid embolism was associated with significantly increased risks of stillbirth and neonatal asphyxia, mechanical ventilation, sepsis, seizures and prolonged length of hospital stay. CONCLUSIONS: Amniotic fluid embolism remains a rare but serious obstetric outcome, with several important modifiable risk factors and major implications for maternal, fetal and neonatal health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".