Pediatric Reference Intervals for Point-of-Care Random Glucose in Healthy Children and Adolescents
Bibliographic record
Abstract
BACKGROUND: Glucose testing at the point-of-care (POC) is routinely used in the diagnosis, prognosis, and monitoring of diabetic states and other clinical conditions. Accurate reference intervals (RIs) are essential in appropriate clinical decision-making. In this study, RIs were established for random glucose (whole blood) in the Canadian Laboratory Initiative on Pediatric Reference (CALIPER) cohort using 2 POC instruments: the Nova Biomedical StatStrip (handheld glucometer) and Radiometer ABL90 FLEX Plus (benchtop instrument). An analytical comparison was also completed between the 2 POC systems and a laboratory-based analyzer (Ortho Vitros 5600). METHODS: Approximately 400 healthy children and adolescents (birth to 18 years) were recruited with informed consent from community schools or clinics providing care to metabolically stable/healthy children. Random venous samples were collected and run sequentially on the Nova Biomedical StatStrip (whole blood), Radiometer ABL90 FLEX Plus (whole blood), and Ortho Vitros 5600 (serum). RIs and method comparisons between analytical platforms were completed according to CLSI guidelines. RESULTS: Significantly different glucose concentrations were observed in infancy, requiring age-specific partitioning (0-<1 month, 1-<6 months, 6 months-<19 years) on all platforms. Excellent concordance was observed between POC platforms (Pearson r > 0.90), with a small negative bias. Good comparability was observed between POC and laboratory-based platforms (Pearson r > 0.80). CONCLUSION: This study established comprehensive pediatric RIs for random glucose (whole blood) on modern POC systems in the CALIPER cohort for the first time. Results demonstrate excellent concordance in glucose values between POC systems and good comparability with a laboratory-based analyzer. These data will assist in more accurate clinical decision-making in pediatric healthcare institutions.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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".