Marked Biological Variance in Endocrine and Biochemical Markers in Childhood: Establishment of Pediatric Reference Intervals Using Healthy Community Children from the CALIPER Cohort
Bibliographic record
Abstract
BACKGROUND: Reference intervals are indispensable in evaluating laboratory test results; however, appropriately partitioned pediatric reference values are not readily available. The Canadian Laboratory Initiative for Pediatric Reference Intervals (CALIPER) program is aimed at establishing the influence of age, sex, ethnicity, and body mass index on biochemical markers and developing a comprehensive database of pediatric reference intervals using an a posteriori approach. METHODS: A total of 1482 samples were collected from ethnically diverse healthy children ages 2 days to 18 years and analyzed on the Abbott ARCHITECT i2000. Following the CLSI C28-A3 guidelines, age- and sex-specific partitioning was determined for each analyte. Nonparametric and robust methods were used to establish the 2.5th and 97.5th percentiles for the reference intervals as well as the 90% CIs. RESULTS: New pediatric reference intervals were generated for 14 biomarkers, including α-fetoprotein, cobalamin (vitamin B12), folate, homocysteine, ferritin, cortisol, troponin I, 25(OH)-vitamin D [25(OH)D], intact parathyroid hormone (iPTH), thyroid-stimulating hormone, total thyroxine (TT4), total triiodothyronine (TT3), free thyroxine (FT4), and free triiodothyronine. The influence of ethnicity on reference values was also examined, and statistically significant differences were found between ethnic groups for FT4, TT3, TT4, cobalamin, ferritin, iPTH, and 25(OH)D. CONCLUSIONS: This study establishes comprehensive pediatric reference intervals for several common endocrine and immunochemical biomarkers obtained in a large cohort of healthy children. The new database will be of global benefit, ensuring appropriate interpretation of pediatric disease biomarkers, but will need further validation for specific immunoassay platforms and in 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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".