Risk factors for pre‐eclampsia in clinical practice guidelines: Comparison with the evidence
Bibliographic record
Abstract
OBJECTIVE: To compare pre-eclampsia risk factors identified by clinical practice guidelines (CPGs) with risk factors from hierarchical evidence review, to guide pre-eclampsia prevention. DESIGN: Our search strategy provided hierarchical evidence of relationships between risk factors and pre-eclampsia using Medline (Ovid), searched from January 2010 to January 2021. SETTING: Published studies and CPGs. POPULATION: Pregnant women. METHODS: We evaluated the strength of association and quality of evidence (GRADE). CPGs (n = 15) were taken from a previous systematic review. MAIN OUTCOME MEASURE: Pre-eclampsia. RESULTS: Of 78 pre-eclampsia risk factors, 13 (16.5%) arise only during pregnancy. Strength of association was usually 'probable' (n = 40, 51.3%) and the quality of evidence was low (n = 35, 44.9%). The 'major' and 'moderate' risk factors proposed by 8/15 CPGs were not well aligned with the evidence; of the ten 'major' risk factors (alone warranting aspirin prophylaxis), associations with pre-eclampsia were definite (n = 4), probable (n = 5) or possible (n = 1), based on moderate (n = 4), low (n = 5) or very low (n = 1) quality evidence. Obesity ('moderate' risk factor) was definitely associated with pre-eclampsia (high-quality evidence). The other ten 'moderate' risk factors had probable (n = 8), possible (n = 1) or no (n = 1) association with pre-eclampsia, based on evidence of moderate (n = 1), low (n = 5) or very low (n = 4) quality. Three risk factors not identified by the CPGs had probable associations (high quality): being overweight; 'prehypertension' at booking; and blood pressure of 130-139/80-89 mmHg in early pregnancy. CONCLUSIONS: Pre-eclampsia risk factors in CPGs are poorly aligned with evidence, particularly for the strongest risk factor of obesity. There is a lack of distinction between risk factors identifiable in early pregnancy and those arising later. A refresh of the strategies advocated by CPGs is needed.
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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 | 0.091 | 0.427 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".