Advances in pediatric reference intervals for biochemical markers: Establishment of the caliper database in healthy children and adolescents
Bibliographic record
Abstract
Clinical laboratory reference intervals provide valuable information to medical practitioners in their interpretation of quantitative laboratory test results, and therefore are critical in the assessment of patient health and in clinical decisionmaking. The reference interval serves as a health-associated benchmark with which to compare an individual test result. Unfortunately, critical gaps currently exist in accurate and upto-date pediatric reference intervals for accurate interpretation of laboratory tests performed in children and adolescents. These critical gaps in the available laboratory reference intervals have the clear potential of contributing to erroneous diagnosis or misdiagnosis of many diseases. To address these important gaps, several initiatives have begun internationally by a number of bodies including the KiGGS initiative in Germany, the Aussie Normals in Australia, the AACC-National Children Study in USA, the NORIc HiLD Initiative in Scandinavia, and the CALIPER study in Canada. In the present article, we will review the gaps in pediatric reference intervals, challenges in establishing pediatric norms in healthy children and adolescents, and the major contributions of the CALIPER program to closing the gaps in this crucial area of pediatric laboratory medicine. We will also discuss the recently published CALIPER reference interval database (www.caliperdatabase.com) developed to provide comprehensive age and gender specific pediatric reference intervals for a larger number of biochemical markers, based on a large and diverse healthy children cohort. The CALIPER database is based on a multiethnic population examining the influence of ethnicity on laboratory reference intervals. Thus the database has proved to be of global benefit and is being adopted by hospital laboratories worldwide.
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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 | 0.041 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".