The Canadian laboratory initiative on pediatric reference intervals: A CALIPER white paper
Bibliographic record
Abstract
Laboratory investigations provide physicians with objective data to aid in disease diagnosis, clinical decision making, and patient follow up. Clinical interpretation of laboratory test results relies heavily on the availability of appropriate population-based reference intervals (i.e. normative values) or decision limits developed through clinical outcome studies. Although reference intervals are fundamental to accurate laboratory test interpretation, and thus critically important to healthcare, the need for sound evidence-based reference intervals has been largely overlooked, particularly in the pediatric population. In the field of pediatric laboratory medicine, accurate age- and sex-specific reference intervals established using samples from healthy children and adolescents have not been readily available, forcing many clinical laboratories to report adult reference intervals with pediatric test results. When pediatric reference intervals are available, they have often been established with a small sample size, inpatient or outpatient samples, outdated methodologies, and/or inappropriate statistical procedures. To address these unacceptable limitations, several national and global initiatives have begun to close the critical evidence gaps in pediatric reference intervals. Notably, the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) has made significant strides towards improving pediatric healthcare in Canada and globally. The present report is a white paper summarizing CALIPER, and provides a comprehensive compendium of the data generated through this project over the past decade as a single resource for clinical laboratory specialists, clinicians, and other healthcare workers. CALIPER launched an outreach campaign in 2008 to recruit healthy community children and adolescents, and developed a robust statistical algorithm, in accordance with the Clinical and Laboratory Standards Institute (CLSI) guidelines, to develop accurate age- and sex-specific pediatric reference intervals. The first CALIPER direct reference interval study was published in 2012, with age- and sex-specific reference intervals reported for 40 common biochemical markers. To date, CALIPER has collected health information and blood samples from over 9700 community children and adolescents, and has established a comprehensive database of age- and sex-specific reference intervals for over 100 biomarkers of pediatric disease. CALIPER has also performed a series of transference and verification studies to expand the applicability of the CALIPER database to five major analytical platforms, including Abbott, Beckman, Ortho, Roche, and Siemens. Through novel knowledge translation initiatives, the CALIPER Reference Interval Database has been made freely available online ( www.caliperproject.ca ) as well as on a mobile application (CALIPER Reference App), and it is used by clinical laboratories across Canada, the United States, and globally. In addition to establishing this comprehensive pediatric reference interval database, CALIPER has also performed a series of sub-studies, including examining how reference intervals are affected by pre-analytical factors (i.e. sample stability at specific storage conditions, fasting status and time of sample collection), biological variation (i.e. intraindividual and interindividual biological variation, reference change values), and ethnicity and pubertal development stage. In this white paper, extensive tables of pediatric reference intervals are provided for easy reference for clinical laboratories worldwide. All data reported have been published in over 20 peer reviewed publications and are also available through the CALIPER Reference Interval Database as well as the CALIPER Reference App for mobile devices.
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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 | 0.077 | 0.144 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.019 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.011 | 0.004 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".