Continuous reference curves for common hematology markers in the CALIPER cohort of healthy children and adolescents on the Sysmex XN‐3000 system
Bibliographic record
Abstract
INTRODUCTION: Clinicians and healthcare professionals rely heavily on health-associated standards, such as reference intervals (RIs), for appropriate laboratory test result interpretation. RIs are commonly partitioned into discrete age/sex bins based on statistical and/or clinical significance. In pediatric hematology, such partitioning does not adequately represent complex variation in analyte concentrations throughout maturation. The objective of this study was to establish continuous RIs for common hematological parameters in the healthy pediatric Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) cohort. METHODS: Data from healthy CALIPER children and adolescents (6 months-<19 years) were used to generate continuous RIs (ie, 2.5th and 97.5th quantiles) for 19 hematological parameters. Continuous curves were statistically established with nonparametric quantile regressions. Flagging rate analysis was completed for the established continuous upper and lower reference limits and subsequently compared to previously published discrete CALIPER reference intervals for all parameters. RESULTS: Continuous RIs were established for 19 hematology parameters, where seven required sex-specific reference curves. Based on flagging rate assessment, continuous RIs appear to more accurately estimate hematological reference limits over the pediatric age range, especially for analytes with complex age- and sex-specific reference value patterns. CONCLUSIONS: This is the first study to generate continuous RIs for a breadth of hematological markers in a healthy pediatric Canadian population. The increased power of continuous reference intervals to accurately estimate the complex relationship between hematological analyte concentration and age during a time of extensive growth and development is expected to improve laboratory test result interpretation and, subsequently, pediatric clinical decision-making.
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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 | 0.002 |
| 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".