AI 中文总结
该研究提出线性高斯因子模型，对977例胎儿的四类测量数据拟合后发现测量模块几乎独立，可通过边缘化缺失值得到反映可用数据不确定性的生长谱，其重建残差还可用于数据质量筛选。
AI 中文摘要
目的：常规孕晚期检查会获取胎儿生物测量、母体、多普勒及胎儿心脏测量数据，这些数据由临床判断决定，因此往往不完整。我们发现这四个测量模块几乎相互无信息，这一单一特性决定了其表示能进行的插补、审核，以及仅胎儿大小无法指示的内容。方法：对977例胎儿（其中169例为小于胎龄儿SGA，61例为重度SGA；77例为大于胎龄儿LGA）的四个模块中的25项测量数据，拟合线性高斯因子模型（K=8，通过平行分析确定，采用VARIMAX旋转）。后验精度仅汇总观测测量的贡献，因此缺失值被边缘化而非插补。使用每项测量与其重建值之间的标准化残差筛选数据质量。结果：用其他三个模块预测任意一个模块的折外R²为0.023。该表示是连续生长谱，无聚类结构（Hartigan dip p=0.99，gap统计量k=1，三聚类轮廓系数0.07），按出生体重百分位对胎儿排序（Spearman相关系数rho=0.55）。在169例SGA胎儿中，血流动力学再分配轴区分了25项不良结局（AUC=0.70，95%置信区间0.585-0.808），而测量大小的区分能力为0.60（95%置信区间0.451-0.738）。当多普勒数据被删失时，边缘化区间对保留测量的覆盖比例，相对于名义95%和90%置信水平，分别为97%和93%。重建残差标记了977条记录中的38条，其中36条经确认是登记系统的转录错误。结论：对缺失测量进行边缘化得到的表示，其不确定性反映了可用数据，且其重建残差可兼作数据质量筛选工具。由于各模块几乎独立，确认的标记为模块内错误，合成基准提供了跨模块检测可用前所需的耦合关系。
英文摘要
Objective: Routine third-trimester examination yields fetal biometry, maternal, Doppler and fetal-cardiac measurements, acquired at clinical discretion and therefore often incomplete. We show that these four measurement blocks are close to mutually uninformative, and that this single property determines what a representation of them can impute, what it can audit, and what fetal size alone cannot indicate. Methods: A linear-Gaussian factor model (K = 8 by parallel analysis, VARIMAX-rotated) was fitted to 25 measurements in four blocks from 977 fetuses (169 SGA, 61 severe; 77 LGA). Posterior precision sums contributions from observed measurements only, so missing values are marginalized rather than imputed. Data quality was screened using the standardized residual between each measurement and its reconstruction. Results: Predicting any one block from the other three gives an out-of-fold R2 of 0.023. The representation is a continuous growth spectrum with no cluster structure (Hartigan dip p = 0.99, gap statistic k = 1, three-cluster silhouette 0.07) ordering fetuses by birthweight centile (Spearman rho = 0.55). Among 169 SGA fetuses the haemodynamic redistribution axis separated the 25 adverse outcomes (AUC 0.70, 0.585-0.808) where measured size did not (0.60, 0.451-0.738). With Doppler censored, the marginalized interval covered held-out measurements in 97% and 93% of cases against nominal 95% and 90%. The reconstruction residual flagged 38 of 977 records, 36 confirmed transcription errors in the registry. Conclusion: Marginalizing missing measurements yields a representation whose uncertainty reflects the available data, and whose reconstruction residual doubles as a data-quality screen. Because the blocks are nearly independent, confirmed flags are within-block errors, and a synthetic benchmark gives the coupling needed before cross-block detection becomes available.
CommentsWithdrawn by the authors. The manuscript was posted without the agreement of all listed co-authors
