Etiology of stillbirth in a tertiary care center: a retrospective cohort study assessing ultrasound, laboratory, and pathology investigations
Bibliographic record
Abstract
BACKGROUND: Canadian stillbirth data are limited, and a significant proportion of pregnancies resulting in stillbirth have no attributable cause. The objective of this study was to characterize stillbirth case investigations and management at a tertiary care hospital in Ontario, Canada. METHODS: This was a retrospective chart review study of all cases of singleton stillbirth at The Ottawa Hospital between 1 January 2012 and 31 December 2017. Terminations and multiples stillbirths were excluded. Chart reviews were conducted to extract maternal sociodemographic, obstetrical, and fetal characteristics, including results from antenatal ultrasounds, autopsy, placenta pathology, and laboratory investigations. RESULTS: A total of 155 eligible cases of stillbirth were identified, resulting in a 6-year stillbirth rate of 4.2 per 1000 total births. The median maternal age was 31.0 years (IQR: 29.0, 35.0) and the median gestational age at delivery was 28 weeks (IQR: 24, 35). A total of 9 (5.8%) pregnant individuals had a history of previous stillbirth. Of the 155 stillbirths, 35% underwent the full suite of post-loss laboratory, placental, and fetal autopsy investigations. 63.2% of cases had post-loss laboratory investigations completed. 76% and 71% of cases had fetal autopsy and placenta pathology evaluations completed, respectively. Antenatal characteristics associated with stillbirth included fetal anomalies/genetic markers (27.1%), umbilical cord and placental anomalies (24.5%), fetal growth abnormalities (27.7%), cervical/uterine abnormalities (11.6%), and amniotic fluid abnormalities (25.1%). The most common autopsy findings included evidence of infection (22.7%), fetal anomalies (12.6%), and fetal hypoxia (10%). The most common placental pathology findings included features of placental insufficiency (21.8%), retroplacental abnormalities (16.3%), and umbilical cord accident/infarct (15.4%). CONCLUSIONS: Our findings demonstrate that as many as two-thirds of singleton stillbirth cases at our center did not receive the post-perinatal loss investigations recommended by clinical practice guidelines. More thorough collection of post-stillbirth data at all levels (institutional, provincial, national) is warranted to improve our understanding of stillbirth epidemiology, etiology, and management in Canada.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".