Myocardial infarction in pregnancy and postpartum in the UK
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIM: Cardiac disease is a leading cause of maternal death in the developed world, responsible for one-fifth of all maternal deaths in the UK. The aim of this study was to estimate the incidence of myocardial infarction (MI) in pregnancy and up to one week postpartum in the UK and describe risk factors, management and outcomes. METHODS: A prospective population-based study with nested case control analysis used the UK Obstetric Surveillance System to identify all women in the UK with MI in pregnancy (in the years 2005-2010). A control group of 1360 women was used for comparison. Multivariable unconditional logistic regression was conducted to identify potential risk factors for MI in pregnancy and calculate adjusted odds ratios with 95% confidence intervals. RESULTS: Twenty-five cases of MI in pregnancy were reported, giving an estimated incidence of 0.7 per 100,000 maternities (95%CI 0.5-1.1). Maternal age, smoking, hypertension, twin pregnancy and pre-eclampsia were independently associated with MI in pregnancy. Fifteen (60%) women underwent coronary angiography; nine (60%) had coronary atherosclerosis, three (21%) had coronary artery dissection, one (7%) had a coronary thrombus and two (13%) had normal coronary arteries. Nine women had angioplasty +/- stenting and two were thrombolysed. No women died. CONCLUSIONS: Many risk factors are both recognisable and modifiable. Management of MI in pregnancy was highly variable indicating a clear need for further information regarding the safety and outcomes of different interventions. The addition of pregnancy status as a compulsory field in cardiac audit databases would enable routine collection of this information.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it