Dried Blood Spot Reference Intervals for Steroids and Amino Acids in a Neonatal Cohort of the National Children's Study
Bibliographic record
Abstract
BACKGROUND: Reference intervals from children are limited by access to healthy children and their limited blood volumes. In this study we set out to fill gaps in pediatric reference intervals for amino acids and steroid hormones using dried blood spots (DBS) from a cohort of the National Children's Study. METHODS: Deidentified DBS annotated with age, birthweight, sex, and geographic location were obtained from 310 newborns aged 0-4 days and analyzed for 25 amino acids and 4 steroid hormones using LC-MS/MS. Nonparametric statistical approaches were used to generate the 2.5th-97.5th percentile distributions for newborns. Paired plasma/DBS specimens were used to mathematically transform DBS reference intervals to corresponding plasma intervals. RESULTS: 10 of 25 DBS amino acid distributions were dependent on sex. There was little correlation with age, birthweight, or geographic location over the first 4 days of life. In most cases, transformation of DBS distributions to plasma distributions faithfully reflected independent studies of newborn plasma amino acid distributions. In general newborn steroid distributions were negatively correlated with age and birthweight over the first 4 days of life. Data distributions for the 4 steroids were not found related to geographic location, but testosterone concentrations displayed sex dependence. Transformation of DBS distributions to plasma intervals did not faithfully replicate other neonate steroid reference intervals determined directly with plasma. CONCLUSIONS: These data demonstrate the feasibility and utility of deriving newborn reference intervals from large numbers of archived DBS samples such as those obtained from the National Children's Study biobank.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".