Closing the Gaps in Pediatric Laboratory Reference Intervals: A CALIPER Database of 40 Biochemical Markers in a Healthy and Multiethnic Population of Children
Bibliographic record
Abstract
BACKGROUND: Pediatric healthcare is critically dependent on the availability of accurate and precise laboratory biomarkers of pediatric disease, and on the availability of reference intervals to allow appropriate clinical interpretation. The development and growth of children profoundly influence normal circulating concentrations of biochemical markers and thus the respective reference intervals. There are currently substantial gaps in our knowledge of the influences of age, sex, and ethnicity on reference intervals. We report a comprehensive covariate-stratified reference interval database established from a healthy, nonhospitalized, and multiethnic pediatric population. METHODS: Healthy children and adolescents (n = 2188, newborn to 18 years of age) were recruited from a multiethnic population with informed parental consent and were assessed from completed questionnaires and according to defined exclusion criteria. Whole-blood samples were collected for establishing age- and sex-stratified reference intervals for 40 serum biochemical markers (serum chemistry, enzymes, lipids, proteins) on the Abbott ARCHITECT c8000 analyzer. RESULTS: Reference intervals were generated according to CLSI C28-A3 statistical guidelines. Caucasians, East Asians, and South Asian participants were evaluated with respect to the influence of ethnicity, and statistically significant differences were observed for 7 specific biomarkers. CONCLUSIONS: The establishment of a new comprehensive database of pediatric reference intervals is part of the Canadian Laboratory Initiative in Pediatric Reference Intervals (CALIPER). It should assist laboratorians and pediatricians in interpreting test results more accurately and thereby lead to improved diagnosis of childhood diseases and reduced patient risk. The database will also be of global benefit once reference intervals are validated in transference studies with other analytical platforms and local populations, as recommended by the CLSI.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".