Prevention of pre‐eclampsia with aspirin: A systematic review of guidelines and evaluation of the quality of recommendation evidence
Bibliographic record
Abstract
BACKGROUND: Evidence has shown significant benefits of aspirin for preventing pre-eclampsia. OBJECTIVES: The objective of this study was to systematically review recommendations from clinical practice guidelines and other recommendation documents on aspirin for the prevention of pre-eclampsia. SEARCH STRATEGY: Ten databases were searched for statements from December 1, 2013, to January 1, 2022. SELECTION CRITERIA: Without language restrictions, the most recent version of documents was considered. DATA COLLECTION AND ANALYSIS: Two authors independently extracted recommendations. Guideline quality was assessed using a modified AGREE-II instrument and the AGREE-REX tool. MAIN RESULTS: Out of 48 statements on the prevention of pre-eclampsia, 46 had recommendations on use of aspirin. Of them, 39 were supported by evidence from systematic reviews or randomized controlled trials. Three statements reported aspirin's significant reductions in preterm pre-eclampsia and one in perinatal death. Concerning quality, 41% of statements were rated as high quality in all domains of the AGREE-II tool, 15% were rated high quality in all domains of the AGREE-REX tool, and 11% were rated high quality in all domains on both tools. CONCLUSIONS: While 96% of statements advocated for use of aspirin, only 9% reported a significant reduction in preterm pre-eclampsia or perinatal death. Based on the AGREE tools, future statements could use methodological improvement.
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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 | 0.034 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".