Indirect Estimation of Pediatric Between-Individual Biological Variation Data for 22 Common Serum Biochemistries
Bibliographic record
Abstract
OBJECTIVES: Derivation of between-individual biological variation (CVg) data requires repeat sampling of the same subject, which is undesirable and challenging in children. We describe an indirect sampling (data mining) approach to obtain these data in children. METHODS: Twenty-two serum biochemistry results from 6,989 children, who visited their primary care physician in Queensland, Australia, and were tested only twice within a year were included. The CVg and index of individuality of the boys and girls were estimated by year of age, according to the procedures recommended by Fraser and Harris. RESULTS: The CVg was generally higher during the first year of life and declined to reach a constant level by age 4 to 6 years, except for aspartate aminotransferase, alanine aminotransferase, γ-glutamyltransferase, and phosphate. The CVg for these tended to increase after age 10 years. Most of the serum biochemistries examined in this study had indices of individuality 0.6 or less, except sodium, anion gap, bicarbonate, and chloride, which ranged from 0.6 to 1.4. The indices of individuality were very stable across all ages. CONCLUSIONS: These data are comparable to those reported by the Canadian Laboratory Initiative on Pediatric Reference Intervals study and the Ricos database for adults. This study reports the CVg trends and data for boys and girls by year of age, which have not been described previously.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".