Pediatric reference intervals for serum neurofilament light and glial fibrillary acidic protein using the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) cohort
Bibliographic record
Abstract
OBJECTIVES: Blood biomarkers have the potential to transform diagnosis and prognosis for multiple neurological indications. Establishing normative data is a critical benchmark in the analytical validation process. Normative data are important in children as little is known about how brain development may impact potential biomarkers. The objective of this study is to generate pediatric reference intervals (RIs) for serum neurofilament light (NfL), an axonal marker, and glial fibrillary acidic protein (GFAP), an astrocytic marker. METHODS: Serum from healthy children and adolescents aged 1 to <19 years were obtained from the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) cohort. Serum NfL (n=300) and GFAP (n=316) were quantified using Simoa technology, and discrete RI (2.5th and 97.5th percentiles) and continuous RI (5th and 95th percentiles) were generated. RESULTS: While there was no association with sex, there was a statistically significant (p<0.0001) negative association between age and serum NfL (Rho -0.400) and GFAP (Rho -0.749). Two statistically significant age partitions were generated for NfL: age 1 to <10 years (lower, upper limit; 3.13, 20.6 pg/mL) and 10 to <19 years (1.82, 7.44 pg/mL). For GFAP, three statistically significant age partitions were generated: age 1 to <3.5 years (80.4, 601 pg/mL); 3.5 to <11 years (50.7, 224 pg/mL); and 11 to <19 years (26.2, 119 pg/mL). CONCLUSIONS: Taken together with the literature on adults, NfL and GFAP display U-shaped curves with high levels in infants, decreasing levels during childhood, a plateau during adolescence and early adulthood and increasing levels in seniors. These normative data are expected to inform future pediatric studies on the importance of age on neurological blood biomarkers.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".