CALIPER database of paediatric reference intervals: key milestones and future directions
Bibliographic record
Abstract
Accurately established reference intervals are essential to interpret laboratory test results and assess patient health. Poorly established reference intervals can lead to misdiagnosis, subjecting patients to anxiety, unnecessary testing, and/or infection risk. The clinical importance of reference intervals is well recognised. However, establishing robust reference intervals is a complex process, especially for the paediatric population. Therefore, available reference intervals are often incomplete, cover a limited paediatric age interval, and/or do not consider gender differences. CALIPER, a collaborative study among Canadian paediatric centres, is addressing these critical gaps by determining ageand sex-specific paediatric reference intervals for over 80 biomarkers using samples collected from over 8,500 children and adolescents. These reference intervals established on the Abbott ARCHITECT have been transferred to other major analytical platforms, broadening the utility of the CALIPER database. The effect of diurnal variation, post-prandial effects, biological variation, and storage temperature on analyte concentration has also been assessed. Knowledge translation initiatives, including peer-reviewed publications, an online database, and a smartphone application, allow physicians and laboratory technicians worldwide to easily access the CALIPER database. This project has made great progress in addressing critical knowledge gaps in paediatric reference intervals, ultimately benefiting paediatric healthcare across Canada and globally.
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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 | 0.082 | 0.180 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.013 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".