Feasibility of Hemolytic Disease of the Fetus and Newborn Case Ascertainment and Assessing Its Impact on Prenatal and Postnatal Outcomes: Protocol for Observational Studies
Bibliographic record
Abstract
BACKGROUND: Hemolytic disease of the fetus and newborn (HDFN) is a rare but serious condition caused by maternal-fetal red blood cell antigen incompatibility. In an affected pregnancy, maternal immunoglobulin G antibodies cross the placenta and target fetal or neonatal red blood cells, leading to hemolysis, hyperbilirubinemia, and anemia. Although routine screening and alloimmunization prevention programs have contributed to the decline in HDFN in the United States, further understanding of its epidemiology is still needed. OBJECTIVE: This protocol aims to provide an overview of the study design, methodology, and analytical approach used to investigate the epidemiology, treatment, and health care resource use of HDFN within a large integrated health care system. METHODS: We conducted a retrospective cohort study of pregnant women who received obstetric care in the Kaiser Permanente Southern California (KPSC) health care system from January 1, 2008, to June 30, 2022. To identify HDFN cases, we used a novel methodology developed by KPSC researchers combining structured data and detailed clinical information extracted from unstructured records via a natural language processing-assisted chart review process. Chi-square and Wilcoxon rank sum tests were used to compare the distributions of maternal and infant demographic characteristics, as well as medical and perinatal conditions, by HDFN status. We also evaluated the association between HDFN and adverse perinatal outcomes using logistic regression models. Planned analyses using this unique cohort will include describing the annual prevalence, health care resource use, and treatment patterns of mothers and infants by HDFN status. RESULTS: The study population consisted of 464,711 pregnancies, of which 136 (0.03%) were HDFN cases confirmed by chart review, resulting in 138 (0.03%) births (n=137, 0.99% live births and n=1, 0.01% stillbirth). The mean age at pregnancy was 29.8 (SD 5.7) years, and the population was racially and ethnically diverse. CONCLUSIONS: We present an overview of the methodology developed by KPSC clinicians and researchers on the epidemiology, treatment, and health care resource use of HDFN within a large and demographically diverse population of pregnant women. Our novel methodology, combining both structured and unstructured data and a natural language processing-assisted chart review process, ensures the successful identification of true cases to carry out pharmaco-epidemiological studies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/77836.
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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 | 0.095 | 0.097 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".