Paediatric reference intervals for 17 Roche cobas 8000 e602 immunoassays in the CALIPER cohort of healthy children and adolescents
Bibliographic record
Abstract
Background The diagnostic utility of laboratory tests in paediatric medicine relies heavily on the availability of appropriate reference intervals (RIs). The Canadian Laboratory Initiative on Paediatric Reference Intervals (CALIPER) has established a comprehensive database of covariate-stratified RIs for many paediatric laboratory tests using a large, healthy reference population. Several automated analysers in widespread use in clinical laboratories have already been studied. Here, we extend the testing to Roche immunoassays and report, for the first time, comprehensive paediatric RIs for 17 endocrine and special chemistry markers. Methods A total of 741 healthy children and adolescents (1 day to <19 years) were recruited and serum samples were analysed for 17 immunoassays on the Roche cobas 8000 e602 Immunoassay Analyzer. Age and sex-specific RIs were established and corresponding 90% confidence intervals (CIs) were calculated in accordance with Clinical and Laboratory Standards Institute guidelines. Results Reference values for all analytes measured required age partitioning, particularly during early life and throughout adolescence. Of the 17 analytes measured, eight required sex partitioning, including ferritin, thyroid stimulating hormone (TSH), total triiodothyronine (TT3) and all fertility/sex hormones, except prolactin. Conclusions This is the first study to determine accurate paediatric RIs for Roche immunoassays. RIs were generally similar to those previously published by CALIPER on other analytical platforms, highlighting the reproducibility of age- and sex-specific trends in reference values observed across the paediatric age range. The RIs established in this study will improve the accuracy of test result interpretation and clinical decision-making in clinical laboratories utilising Roche immunoassays.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".