Dear Dr. Papageorghiou,
The recent article by Self et al. [1] provides valuable insights into the accuracy of various models for estimating gestational age in the later stages of pregnancy. The authors should be commended for their rigorous methodology and comprehensive analysis of a large data set. However, we believe that the representativeness of the study population deserves further discussion, as it has significant implications for the generalisability and clinical application of the findings.
The study included pregnant women who had received early antenatal care and undergone crown‐rump length (CRL) measurement at 11–14 weeks. While this approach ensures a reliable reference for gestational age, it may not accurately represent the full spectrum of pregnant women encountered in clinical practice, particularly in underprivileged areas. Women who initiate antenatal care later in pregnancy, often due to socioeconomic factors or limited access to health care, may differ systematically from those who receive early and regular care [2]. These differences can encompass nutritional status, comorbidities and overall health, which may influence foetal growth patterns and, consequently, the accuracy of gestational age estimation based on biometric parameters.
Furthermore, the study's findings are most relevant for women who lack early CRL measurements and require gestational age estimation later in pregnancy. Ironically, this group is underrepresented in the study population. Women who seek antenatal care late are often at higher risk of adverse outcomes and require accurate gestational age estimation to guide critical decisions regarding prenatal diagnosis, risk assessment and intervention [3]. The limited representativeness of the study population hinders the direct application of the findings to this vulnerable group.
The issue of representativeness is particularly pressing in resource‐limited settings, where access to early antenatal care is often scarce. In these contexts, it is crucial to validate and prioritise gestational age estimation models that are not only accurate but also feasible, cost‐effective and reliable across diverse populations [4]. Future research should focus on validating these models in community‐based samples and low‐resource settings to ensure their widest applicability.
Addressing the representativeness of study populations is essential for bridging the gap between research evidence and clinical practice. As clinicians, we must critically appraise the limitations of available evidence and consider its applicability to our specific patient populations. Researchers, in turn, should strive to include diverse and representative samples in their studies, accounting for the varied realities of antenatal care access and utilisation.
In conclusion, while the study by Self et al. [1] provides valuable insights, the representativeness of the study population is a crucial consideration when applying the findings to clinical practice. Addressing this limitation will require a concerted effort from both researchers and clinicians to ensure that gestational age estimation models are validated and applied to benefit all pregnant women, especially those most vulnerable.
Author Contributions
Z.L. and X.Q. contributed equally to this work and should be considered as joint first authors. Z.L., X.Q., and D.W. all contributed to the conceptualization and design of the letter. Original drafting was done by Z.L., X.Q., and D.W., with revision contributions and approval of the final version from Z.L. and D.W. Supervision came from D.W.
Conflicts of Interest
The authors declare no conflicts of interest.
Liang Z., Qin X., and Wang D., “Representativeness of Study Population: A Critical Consideration for Applying Gestational Age Estimation Models,” BJOG: An International Journal of Obstetrics & Gynaecology 133, no. 10 (2026): 1951–1952, 10.1111/1471-0528.18059.
Funding: The authors received no specific funding for this work.
Data Availability Statement
The authors have nothing to report.
References
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The authors have nothing to report.