Pediatric Population Reference Value Distributions for Cancer Biomarkers and Covariate-Stratified Reference Intervals in the CALIPER Cohort
Bibliographic record
Abstract
BACKGROUND: Cancer biomarkers are commonly used in pediatrics to monitor cancer progression, recurrence, and prognosis, but pediatric reference value distributions have not been well established for these markers. The Canadian Laboratory Initiative on Pediatric Reference Intervals (CALIPER) sought to develop a pediatric database of covariate-stratified reference value distributions for 11 key circulating tumor markers, including those used in assessment of patients with childhood or adult cancers. METHODS: Healthy community children from birth to 18 years of age were recruited to participate in the CALIPER project with informed parental consent. We analyzed serum samples from 400-700 children (depending on the analyte in question) on the Abbott Architect ci4100 and established reference intervals for α-fetoprotein (AFP), antithyroglobulin (anti-Tg), human epididymis protein 4 (HE4), cancer antigen 125 (CA125), CA15-3, CA19-9, progastrin-releasing peptide (proGRP), carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and total and free prostate specific antigen (PSA) according to CLSI C28-A3 statistical guidelines. RESULTS: We observed significant fluctuations in biomarker concentrations by age and/or sex in 10 of 11 biomarkers investigated. Age partitioning was required for CA153, CA125, CA19-9, CEA, SCC, proGRP, total and free PSA, HE4, and AFP, whereas sex partitioning was also required for CA125, CA19-9, and total and free PSA. CONCLUSIONS: This CALIPER study established a database of childhood reference intervals for 11 tumor biomarkers and revealed dramatic fluctuations in tumor marker concentrations between boys and girls and throughout childhood. In addition, important differences between the adult and pediatric population were observed, further highlighting the need for pediatric-specific reference intervals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.000 |
| 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".