FORGE-CTG: Outcome Queries and Dual-View Distillation for Intrapartum Fetal Acidosis Detection

Intrapartum cardiotocography (CTG) is used for fetal surveillance, yet detecting neonatal acidaemia from fetal heart-rate and uterine-contraction traces remains difficult. A pathological trace can trigger intervention without acidaemia, while electrode loss, motion artefact, or prolonged gaps can obscure a trace. Existing learning pipelines face limitations. They reduce a continuous physiological outcome to a single pH threshold, discarding severity structure, and treat missing signal as noise to be imputed rather than as a quality condition that should change supervision. To bridge this gap, we introduce FORGE-CTG, a framework built around complementary ideas. SCOS supervises a shared representation through binary risk, rank-consistent ordinal pH thresholds, continuous pH, and masked base deficit. QVD derives reliability from the raw trace and validity mask, then trains a student against an exponential-moving-average teacher reading a conservative repair; uniform patch weighting keeps quality in the teacher’s view without weakening the student’s learning signal. On the CTU-UHB dataset, FORGE-CTG ranks first among 19 models at 0.7283 record-level AUROC and degrades 4.6×more slowly under increasing missingness, reaching ranking performance with half the training labels. The severity-supervision principle also transfers to clinical time-series tasks, with PTB-XL dataset and CPSC2018 dataset experiments showing a 24% reduction in ordinal severity error with perfect rank consistency. Together, these results support a practical direction for clinical monitoring in which richer supervision and explicit responses to signal quality improve the reliability of risk estimates without requiring larger models.