Influence of ethnicity on biochemical markers of health and disease in the CALIPER cohort of healthy children and adolescents
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Bibliographic record
Abstract
Background Accurate pediatric reference intervals (RIs) for laboratory tests determined in a healthy pediatric population are essential for correct laboratory test interpretation and clinical decision-making. In pediatrics, RIs require partitioning by age and/or sex; however, the need for partitioning based on ethnicity is unclear. Here, we assessed the influence of ethnicity on biomarker concentrations in the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) cohort of healthy children and adolescents and compared the results with the National Health and Nutrition Examination Survey (NHANES). Methods A total of 52 biomarkers were measured in a multiethnic population of 846-1179 healthy children (aged 5 to <19 years) upon informed consent. Biomarker concentrations were retrospectively compared between four major ethnic groups (i.e. Black, Caucasian, East Asian, and South Asian, determined by parental ethnicity). Retrospective results were verified prospectively using an additional 500 healthy pediatric samples with equal sample size across ethnicities. Ethnic-specific differences were assessed based on statistical significance and biological and analytical variations. Appropriate age-, sex-, and ethnic-specific RIs were calculated. Results Ethnic-specific differences were not observed for 34 biomarkers examined in the retrospective analysis, while 18 demonstrated statistically significant ethnic differences. Among these, seven analytes demonstrated ethnic-specific differences in the prospective analysis: vitamin D, amylase, ferritin, follicle-stimulating hormone (FSH), immunoglobulin A (IgA), immunoglobulin G (IgG), and immunoglobulin M (IgM). Analysis of select NHANES data confirmed CALIPER findings. Conclusions This is the first comprehensive Canadian pediatric study examining ethnic-specific differences in common biomarkers. While the majority of biomarkers did not require ethnic partitioning, ethnic-specific RIs were established for seven biomarkers showing marked differences. Further studies in other populations are needed to confirm our findings.
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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 | 0.004 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it