Reference Standards for Newborn Screening of Metabolic Disorders by Tandem Mass Spectrometry: A Nationwide Study on Millions of Chinese Neonatal Populations
Bibliographic record
Abstract
Introduction: The major clinical problem presently confronting the Chinese newborn screening (NBS) programs by tandem mass spectrometry (MS/MS) is the lack of comprehensive reference intervals (RIs) for disease biomarkers. To close this gap, the Chinese National Center for Clinical Laboratories (NCCL) launched a nationwide study to investigate the dynamic pattern of 35 MS/MS NBS biomarkers and establish accurate and robust RIs. Methods: Blood spot samples from 4,714,089 Chinese neonates were tested in participating centers/laboratories and used for study analysis. MS/MS NBS biomarker trends were visually assessed by their concentrations over age. Specific partitions were determined arbitrarily by each day and sex or by the statistical method of Harris and Boyd. RIs, corresponding to the 2.5th and 97.5th percentiles, as well as the 1th, 25th, 75th and 99th percentiles were calculated for each reference partition using a non-parametric rank approach. Results: Most MS/MS NBS biomarkers fluctuated during the first week of life, followed by a relatively stable concentration. Age and sex-specific RIs were established and presented an improved specificity over the RIs used in participating centers/laboratories. Females demonstrated higher 2.5th and 97.5th percentiles in all amino acids except arginine and ornithine than males, whereas males showed higher 2.5th and 97.5th percentiles in most acylcarnitines. Conclusion: The present study determined the dynamic trends of 35 MS/MS biomarkers and established age and sex-specific RIs, valuably contributing to the current literature and timely evaluation of neonatal health and disease.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".