A-348 Pediatric reference intervals for 12 enzymes, proteins, and lipids on the Roche cobas® pro Integrated System established using the CALIPER cohort of healthy children
Bibliographic record
Abstract
Abstract Background Reference intervals play a vital role in interpreting laboratory test results, ensuring precise clinical diagnosis and informed decision-making. In pediatric patients, biomarker levels fluctuate considerably throughout growth and development, making the establishment of reliable reference intervals particularly complex. The Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) is committed to developing accurate and extensive pediatric reference intervals for tests conducted on major analytical platforms used by clinical laboratories around the world. Continuing in this mission, the present study directly established pediatric reference intervals for 12 enzymes, proteins, and lipids using the relatively new Roche cobas® pro system on its c503 module. Methods Before conducting reference interval studies, analytical performance was first verified with precision, linearity and patient comparison studies. A total of 300–500 CALIPER samples, obtained with informed consent, were analyzed across the following age groups: 0–1, 1–5, 6–10, 11–14, and 15–18 years. For biomarkers that changed more dynamically with age or sex, additional samples were analyzed to ensure sufficient data for proper partitioning. Statistical analysis, partitioning, and graphing were performed using R software, following the methodology of previous CALIPER studies. Results New pediatric reference intervals were established for albumin, total protein, ALT, AST, ALP, GGT, amylase, CK, LDH, total cholesterol, HDL cholesterol, and triglycerides. Age partitioning was necessary for all analytes, while sex partitioning was not required for albumin, total protein, amylase, GGT, and lipids. As anticipated, reference values varied with age and sex, aligning with previous CALIPER findings from other analytical platforms. Notably, several analytes exhibited significantly different values during the neonatal period, highlighting the need for further sample analysis. Conclusion This study is the first to establish pediatric reference intervals using the direct method for key routine biomarkers related to liver and renal function, as well as nutritional status, on the Roche cobas® pro platform within the CALIPER cohort. Additionally, creatine kinase reference intervals had not previously been available on any Roche platform through CALIPER. The availability of these reference intervals on this modern chemistry analyzer system will support their integration into clinical practice, enhancing the accuracy of pediatric test result interpretation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".