Complex Reference Values for Endocrine and Special Chemistry Biomarkers across Pediatric, Adult, and Geriatric Ages: Establishment of Robust Pediatric and Adult Reference Intervals on the Basis of the Canadian Health Measures Survey
Bibliographic record
Abstract
BACKGROUND: Defining laboratory biomarker reference values in a healthy population and understanding the fluctuations in biomarker concentrations throughout life and between sexes are critical to clinical interpretation of laboratory test results in different disease states. The Canadian Health Measures Survey (CHMS) has collected blood samples and health information from the Canadian household population. In collaboration with the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER), the data have been analyzed to determine reference value distributions and reference intervals for several endocrine and special chemistry biomarkers in pediatric, adult, and geriatric age groups. METHODS: CHMS collected data and blood samples from thousands of community participants aged 3 to 79 years. We used serum samples to measure 13 immunoassay-based special chemistry and endocrine markers. We assessed reference value distributions and, after excluding outliers, calculated age- and sex-specific reference intervals, along with corresponding 90% CIs, according to CLSI C28-A3 guidelines. RESULTS: We observed fluctuations in biomarker reference values across the pediatric, adult, and geriatric age range, with stratification required on the basis of age for all analytes. Additional sex partitions were required for apolipoprotein AI, homocysteine, ferritin, and high sensitivity C-reactive protein. CONCLUSIONS: The unique collaboration between CALIPER and CHMS has enabled, for the first time, a detailed examination of the changes in various immunochemical markers that occur in healthy individuals of different ages. The robust age- and sex-specific reference intervals established in this study provide insight into the complex biological changes that take place throughout development and aging and will contribute to improved clinical test interpretation.
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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 | 0.027 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 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".