Use of Race, Ethnicity, and National Origin in Studies Assessing Cardiovascular Risk in Women With a History of Hypertensive Disorders of Pregnancy
Bibliographic record
Abstract
Women with a history of hyperBtensive disorders of pregnancy (HDP) are at particularly high risk for cardiovascular disease (CVD) and CVD-related death, and certain racial and ethnic subpopulations are disproportionately affected by these conditions. We examined the use of race, ethnicity, and national origin in observational studies assessing CVD morbidity and mortality in women with a history of HDP. A total of 124 studies, published between 1976 and 2021, were reviewed. We found that white women were heavily overrepresented, encompassing 53% of all participants with HDP. There was limited and heterogeneous reporting of race and ethnicity information across studies and only 27 studies reported including race and/or ethnicity variables in at least 1 statistical analysis. Only 2 studies mentioned the use of these variables as a strength; several others (k = 18) reported a lack of diversity among participants as a study limitation. Just over half of included articles (k = 68) reported at least 1 sociodemographic variable other than race and ethnicity (eg, marital status and income); however, none investigated how they might have worked synergistically or antagonistically with race and/or ethnicity to influence participants' risk of CVD. These findings highlight significant areas for improvement in cardiovascular obstetrics research, including the need for more robust and standardized methods for collecting, reporting, and using sociodemographic information. Future studies of CVD risk in women with a history of HDP should explicitly examine racial and ethnic differences and use an intersectional approach.
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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 | 0.064 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".