Closing the Gaps in Pediatric and Maternal Reference Standards for Biomarkers of Health and Disease
Bibliographic record
Abstract
Clinical biomarker assessment is integral to the diagnosis, prognostication, and monitoring of health and disease in pediatrics and during pregnancy. An understanding of the impact of dynamic physiological and metabolic adaptations throughout child growth and development as well as pregnancy on the circulating biochemistry is urgently needed to support evidence-based diagnostics. Inappropriate biomarker interpretation due to lack of high quality evidence significantly increases risk of misinformed clinical decision-making, with serious implications for mother and child. I postulate that pediatric and maternal reference values for the majority of routine and emerging clinical biomarkers change significantly during these periods, necessitating establishment of evidence-based reference intervals in these specific populations. Profiling of 78 biochemical and immunochemical parameters as well as 62 hematological parameters in healthy children and adolescents (birth to <19 years) was completed, resulting in a robust reference database of age- and sex-stratified pediatric reference intervals for clinical biomarkers of health and disease. Data mining techniques were also applied to interrogate a retrospective cohort of 120,000 healthy Canadian pregnant women and evaluate the influence of gestational age on 29 clinical biomarkers in uncomplicated pregnancy. A trimester-specific reference database for biochemical and hematological parameters was established and findings were validated in a newly derived prospective cohort of 104 healthy pregnant women. Findings revealed dynamic reference value patterns across the pediatric and gestational periods for biomarkers of renal, hepatic, thyroid, cardiac, inflammatory, and hematological function, supporting the urgent need for evidence-based reference intervals to improve laboratory assessment and clinical decision making in pediatric and maternal healthcare.
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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 | 0.099 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".