Screening for Life: Perspectives From Adult Metabolic Specialists on Newborn Screening for Inherited Metabolic Diseases
Bibliographic record
Abstract
The number of inherited metabolic diseases (IMDs) in newborn screening (NBS) programs has increased significantly in the past decades. For some of the IMDs included in NBS (e.g., tyrosinemia type I), there are clear and substantial health benefits of NBS, while for others (e.g., very long chain acyl CoA dehydrogenase deficiency and 3-methylcrotonyl CoA carboxylase 1 deficiency), this is less clear as NBS identifies individuals who are asymptomatic or have milder forms of the disease. Therefore, knowledge of the full disease spectrum (including later onset forms) is needed when setting diagnostic metabolite cut-offs for NBS. Insights into the clinical, genetic and biochemical characteristics of different patient subsets can be used to redefine NBS protocols to identify patients with more severe forms of the disease who are most likely to benefit from identification in the newborn period. These insights require life-long monitoring of individuals identified based on symptoms versus those identified by NBS to determine long-term health outcomes and quantify the benefits of NBS. Adult metabolic specialists should be included in the development of NBS programs to provide data from this long-term monitoring and to contribute specific knowledge about later onset phenotypes of the IMDs included in NBS programs. The goal should be to develop NBS programs that identify newborns that benefit from early disease detection and treatment, without increasing psychological, social and management burden for individuals who may develop disease in adulthood with milder phenotype or potentially even not at all.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".