Association of placental pathology and postpartum cardiovascular risk screening following preeclampsia: an observational cohort study
Bibliographic record
Abstract
Objective: To determine the association between placental lesions and lifetime cardiovascular disease (CVD) risk screening at 6 months postpartum following preeclampsia (PE). Design: Observational cohort study. Setting: Tertiary care centres in Ottawa and Kingston, Ontario, Canada. Population: Women diagnosed with PE who received cardiovascular screening at 6 months postpartum. Methods: Placentas from women diagnosed with PE were evaluated for histopathological lesions according to a standardised synoptic data collection form with blinding to clinical outcomes apart from gestational age at delivery. At 6 months postpartum, each participant was screened for cardiovascular risk factors and a lifetime cardiovascular risk score was calculated. A risk score >35% was deemed high risk for lifetime CVD. Main Outcome Measures: The association between placental lesions and lifetime CVD risk was assessed using odds ratios (OR, 95% confidence intervals). Results: Of the 85 participants, 53 (62.4%) screened high-risk for lifetime CVD. High-risk women had more severe lesions of maternal vascular malperfusion (MVM). MVM lesions with a severity score >2 resulted in a 3-fold increased risk of screening high risk for lifetime CVD (OR 3.10 [1.20-7.92]). MVM lesion score >2 was moderately predictive of high-risk screening (AUC 0.63 [0.51,0.75]; sensitivity: 71.8% [54.6,84.4]; specificity: 54.7% [41.5,67.3]). When clinical data was added, the model’s predictive performance improved (AUC 0.73 [0.62,0.84] sensitivity 78.4% [65.4,87.5]; specificity 51.6% [34.8,68.0]). Conclusions: PE women with MVM are more likely to screen high-risk for lifetime CVD compared to women without these lesions. Placenta pathology may provide a unique modality to identify women for postpartum cardiovascular screening.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".