Cost‐effectiveness of first‐trimester screening with early preventative use of aspirin in women at high risk of early‐onset pre‐eclampsia
Bibliographic record
Abstract
OBJECTIVE: Pre-eclampsia (PE) remains a leading cause of maternal and fetal morbidity and mortality. A first-trimester screening algorithm predicting the risk of early-onset PE has been developed and validated. Early prediction coupled with initiation of aspirin at 11-13 weeks in women identified as high risk is effective at reducing the prevalence of early-onset PE. The aim of this study was to evaluate the cost-effectiveness of this first-trimester screening program coupled with early use of low-dose aspirin in women at high risk of developing early-onset PE, in comparison to current practice in Canada. METHODS: A decision analysis was performed based on a theoretical population of 387 516 live births in Canada in 1 year. The clinical and financial impact of early preventative screening using the Fetal Medicine Foundation algorithm for prediction of early-onset PE coupled with early (< 16 weeks) use of low-dose aspirin in those at high risk was simulated and compared with current practice using decision-tree analysis. The probabilities at each decision point and associated costs of utilized resources were calculated based on published literature and public databases. RESULTS: Of the theoretical 387 516 births per year, the estimated prevalence of early PE based on first-trimester screening and aspirin use was 705 vs 1801 cases based on the current practice. This was associated with an estimated total cost of C$9.52 million with the first-trimester screening program compared with C$23.91 million with current practice for the diagnosis and management of women with early-onset PE. This equals an annual cost saving to the Canadian healthcare system of approximately C$14.39 million. CONCLUSIONS: The implementation of a first-trimester screening program for PE and early intervention with aspirin in women identified as high risk for early PE has the potential to prevent a significant number of early-onset PE cases with a substantial associated cost saving to the healthcare system in Canada. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".