Determination of pediatric reference limits for 10 commonly measured autoantibodies
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to establish pediatric reference limits for autoimmune disease markers in the Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) cohort of healthy children and adolescents to support their interpretation and clinical decision making. The CALIPER is a national study of healthy children aiming to close gaps in pediatric laboratory medicine by establishing a robust database of pediatric reference intervals for pediatric disease biomarkers (caliperdatabase.org). METHODS: Healthy children and adolescents (n=123, aged 1-19) were recruited to CALIPER with informed consent. Serum autoantibody testing conducted on the BIO-FLASH analyzer (Werfen, Barcelona, Spain) included anti-dsDNA IgG, anti-Sm IgG, anti-RNP IgG, anti-SSB/La IgG, anti-Ro60 IgG, anti-Ro52 IgG, anti-cardiolipin IgG, anti-MPO IgG, anti-PR3 IgG, and anti-tTG IgA. Pediatric reference limits representing 95th, 97.5th, and 99th percentiles were calculated using the non-parametric rank method according to Clinical Laboratory Standards Institute C28-A3 guidelines. RESULTS: The proportion of samples with results above the lower limit of the analytical measuring range were: anti-cardiolipin IgG 90%, anti-dsDNA 22%, anti-Sm 13%, anti-RNP 0.8%, anti-SSB/La 0%, anti-Ro60 0%, anti-Ro52 0%, anti-MPO 25%, anti-PR3 9%, and anti-tTG IgA 28%. Pediatric reference limits and associated 90% confidence intervals were established for all 10 markers. All autoantibodies could be described by one age range except for anti-cardiolipin IgG and anti-MPO. A sex-specific difference was identified for anti-tTG IgA. CONCLUSIONS: Robust pediatric reference limits for 10 commonly clinically utilized autoimmune markers established herein will allow for improved laboratory assessment and clinical decision making in pediatric patients using the BIO-FLASH assay platform worldwide.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".