The high prevalence and impact of rheumatic heart disease in pregnancy in First Nations populations in a high‐income setting: a prospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To describe the epidemiology of rheumatic heart disease (RHD) in pregnancy in Australia and New Zealand (A&NZ). DESIGN: Prospective population-based study. SETTING: Hospital-based maternity units throughout A&NZ. POPULATION: Pregnant women with RHD with a birth outcome of ≥20 weeks of gestation between January 2013 and December 2014. METHODS: We identified eligible women using the Australasian Maternity Outcomes Surveillance System (AMOSS). De-identified antenatal, perinatal and postnatal data were collected and analysed. MAIN OUTCOME MEASURES: Prevalence of RHD in pregnancy. Perinatal morbidity and mortality. RESULTS: There were 311 pregnancies associated with women with RHD (4.3/10 000 women giving birth, 95% CI 3.9-4.8). In Australia, 78% were Aboriginal or Torres Strait Islander (60.4/10 000, 95% CI 50.7-70.0), while in New Zealand 90% were Māori or Pasifika (27.2/10 000, 95% CI 22.0-32.3). One woman (0.3%) died and one in ten was admitted to coronary or intensive care units postpartum. There were 314 births with seven stillbirths (22.3/1000 births) and two neonatal deaths (6.5/1000 births). Sixty-six (21%) live-born babies were preterm and one in three was admitted to neonatal intensive care or special care units. CONCLUSION: Rheumatic heart disease in pregnancy persists in disadvantaged First Nations populations in A&NZ. It is associated with significant cardiac and perinatal morbidity. Preconception planning and counselling and RHD screening in at-risk pregnant women are essential for good maternal and baby outcomes. TWEETABLE ABSTRACT: Rheumatic heart disease in pregnancy persists in First Nations people in Australia and New Zealand and is associated with major cardiac and perinatal morbidity.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".