Blood pressure thresholds for the diagnosis of hypertensive disorders of pregnancy in sickle cell disease
Bibliographic record
Abstract
Summary In this retrospective cohort study of singleton pregnancies in people with sickle cell disease (SCD) delivered at two academic centres between 1990 and 2021, we collected demographic and SCD‐related data, pregnancy outcomes, and the highest systolic and diastolic blood pressure (SBP and DBP) at seven time periods. We compared the characteristics of subjects with new or worsening proteinuria (NWP) during pregnancy to those without. We then constructed receiver operating characteristic (ROC) curves to determine the blood pressure (BP) that best identifies those with NWP. The SBP or DBP thresholds which maximized sensitivity and specificity were 120 mmHg SBP (sensitivity: 55.2%, specificity: 73.5%) and 70 mmHg DBP (sensitivity: 27.6%, specificity: 67.7%). The existing BP threshold of 140/90 mmHg lacked sensitivity in both genotype groups (HbSS/HbSβ 0 : SBP = 21% sensitive, DBP = 5.3% sensitive; HbSS/HbSβ + : SBP = 10% sensitive, DBP = 0% sensitive). Finally, percent change in SBP, DBP and MAP were all poor tests for identifying NWP. Existing BP thresholds used to diagnose hypertensive disorders of pregnancy (HDP) are not sensitive for pregnant people with SCD. For this population, lowering the BP threshold that defines HDP may improve identification of those who need increased observation, consideration of early delivery and eclampsia prophylaxis.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".