Long-term cardiovascular disease after pre-eclampsia: time to move from epidemiology to action
Bibliographic record
Abstract
This editorial refers to `Update on long-term cardiovascular risk after pre-eclampsia: a systematic review and meta-analysis', by Annalisa Inversetti et al. (see page 4). Pre-eclampsia is a complex multi-system disease of pregnancy characterized by placental dysfunction and angiogenic imbalance, which cause maternal vascular endothelial injury, hypertension, and end-organ damage. An estimated 4 million women are diagnosed with pre-eclampsia each year, with this condition causing the deaths of >70 000 women and 500 000 babies worldwide.1 Additionally, pre-eclampsia has a very high burden of short-term maternal and neonatal morbidity. Our understanding of the association between pre-eclampsia and long-term cardiovascular disease has evolved rapidly in the last few decades, alongside the accumulating evidence that cardiovascular disease is the leading cause of death in women worldwide.2 The strong association between pre-eclampsia, cardio-metabolic risk factors, and the future risk of developing cardiovascular disease is now well recognized.3,4 There is even some evidence that the link between pre-eclampsia and premature cardiovascular disease may be independent of other concomitant cardiovascular risk factors.5
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".