Universal cervical‐length screening to prevent preterm birth in twin pregnancy: cost‐utility analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this cost-utility analysis was to model the clinical and economic impact of three cervical-length screening strategies among low-risk twin pregnancies: two-step universal screening (at 18-20 and 20-22 weeks), one-step universal screening (at 18-20 weeks) and no screening. METHODS: This study used a decision-analytic model (decision tree and cohort state transition model) with a 100-year time horizon in a Canadian context. The population included dichorionic diamniotic twin pregnancies without a history of preterm birth or prophylactic progesterone or cerclage. The model assumed that vaginal progesterone was initiated for cervical length ≤ 25 mm and that cervical cerclage was performed plus vaginal progesterone treatment for cervical length ≤ 15mm. The primary outcomes were total lifetime health-related costs (in 2023 Canadian dollars ($)), quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Clinical outcomes included the probability of preterm birth (≤ 28 and ≤ 34 weeks), probability of stillbirth and life expectancy. Probabilistic and deterministic sensitivity analyses were carried out. RESULTS: Base-case and probabilistic sensitivity analysis showed that, when compared with no screening, the two-step screening strategy increased the QALYs modestly (0.62 (95% credible interval (CrI), -0.16 to 1.41)) and decreased lifetime costs (-$2460 (95% CrI, -$4850 to $251)) by reducing the rate of preterm birth. The one-step screening strategy, although inferior to the two-step screening strategy, also increased the QALYs and reduced costs. Findings consistent with these were obtained on testing of the model assumptions with deterministic sensitivity analysis. CONCLUSIONS: This cost-utility analysis supports a universal two-step screening strategy for twin pregnancies in a Canadian context. Although the conclusions of this analysis are robust in terms of the sensitivity analysis, more reliable predictions of long-term costs and quality of life require more twin-specific lifetime data. Additionally, cost-utility analyses in other healthcare contexts are needed. © 2025 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".