Unmet Clinical Needs and Remaining Challenges of Pregnancy Reference Intervals
Bibliographic record
Abstract
BACKGROUND: Pregnancy is characterized by dynamic physiological changes that alter the concentrations of many maternal blood biomarkers. Reporting results against nonpregnant reference values can lead to misinterpretation, diagnostic error, and inappropriate clinical management. The use and reporting of pregnancy-specific reference intervals (RIs) by laboratories is not yet routine practice. CONTENT: This review underscores the critical need for pregnancy RIs to support accurate diagnosis, effective patient care, and optimal clinical decision-making in pregnancy and highlights unique considerations and challenges specific to pregnancy RI studies. Aspects such as defining inclusion/exclusion criteria and participant engagement are more complex in pregnant cohorts. Logistical and resource constraints must be anticipated when undertaking these studies. The current landscape of pregnancy RIs is summarized, drawing upon the literature, which shows substantial heterogeneity in study designs, populations, analytical methods, and partitioning strategies, with important details often missing or insufficient. These issues limit the comparability of findings between studies and the application of published RIs to other pregnant populations. Indirect RI approaches combined with clinical databases provide promising alternatives to traditional direct studies, which help overcome some of the barriers, particularly around recruitment. Experience and lessons learned from the authors' own involvement in prospective and retrospective studies for chemistry and hematology biomarkers are shared. SUMMARY: The challenges associated with developing pregnancy RIs require coordinated and uniform efforts. The discussion herein will help guide future work and knowledge translation to ensure high-quality, standardized studies generate pregnancy RIs that are widely applicable and support maternity care providers and patients alike.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".