Blood Pressure and Retinal Microvascular Characteristics During Pregnancy
Bibliographic record
Abstract
Changes in maternal blood pressure during pregnancy are associated with poor maternal and neonatal outcomes. We investigated whether maternal blood pressure during midpregnancy has an impact on the retinal microcirculation among pregnant Asian women. A total of 665 pregnant women aged 18 to 46 years were recruited from the Growing Up in Singapore Towards Healthy Outcomes Study. Blood pressure and retinal vascular parameters were both measured at 26 weeks' gestation following a standardized protocol. Blood pressure was measured by a digital automatic blood pressure monitor (Omron HEM 705 LP). Quantitative retinal vascular parameters were assessed by a semiautomated computer-based program (Singapore I Vessel Assessment, version 3.0). In multiple linear regression models, every 10-mm Hg increase in mean arterial blood pressure was associated with a 1.9-μm (P<0.001) reduction in retinal arteriolar caliber, a 0.9° (P=0.05) reduction in retinal arteriolar branching angle, and a 0.07 (P<0.01) reduction in retinal arteriolar fractal dimension, respectively. Patients classified into a high-risk group in developing preeclampsia (mean arterial blood pressure ≥ 90 mm Hg) were twice as likely (odds ratio 2.1 [95% CI, 1.0-4.4]) to have generalized retinal arteriolar narrowing compared with those classified into a low-risk group (mean arterial blood pressure <90 mm Hg). Retinal venular caliber and vascular tortuosity were not associated with blood pressure measures. Elevated blood pressure is associated with a range of retinal arteriolar changes in pregnant women, providing evidence for an impact of blood pressure on the microcirculation during pregnancy.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".