OP09.09: The EVA study secondary outcomes: the impact of pre‐eclampsia onset and severity on early vascular aging
Bibliographic record
Abstract
Assess the impact of time of onset and severity of pre-eclampsia (PE) on arterial hemodynamics after primary outcomes showed significant correlation between history of PE and early vascular aging (EVA). Among the 40 women with history of PE, subgroups compared those with severe PE and early-onset PE to controls. Validated methodology characterised arterial hemodynamics by combining arterial tonometry with transthoracic echocardiogram outlining measures of aortic stiffness, steady and pulsatile arterial load. We used one-way ANOVA and multivariable linear regression to adjust for confounders. Table 1 summarises unadjusted differences in arterial hemodynamics between severe PE and early-onset PE versus controls. After adjusting for confounders, severe PE group remained significantly associated with higher aortic stiffness and systemic vascular resistance. All markers of pulsatile and steady arterial load remained significantly higher in women with early-onset PE after adjusting for confounders. We found no statistically significant difference when comparing non-severe or late-onset PE and controls. In this secondary analysis of the EVA study, we found that EVA was only present in subgroups of women who had severe features of PE or early-onset of the disease. These women are at higher risk of future cardiovascular events and may benefit from early risk stratification and targeted interventions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.001 |
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".