Newborn Screening Conditions: Early Intervention and Probability of Developmental Delay
Bibliographic record
Abstract
OBJECTIVES: The purpose of this study is to explore which newborn screening (NBS) conditions are automatically eligible for early intervention (EI) across states and to determine the extent to which each disorder should automatically qualify for EI because of a high probability of developmental delay. METHODS: We examined each state's EI eligibility policy and reviewed the literature documenting developmental outcomes for each NBS condition. Using a novel matrix, we assessed the risk of developmental delay, medical complexity, and risk of episodic decompensation, revising the matrix iteratively until reaching consensus. Three NBS conditions (biotinidase deficiency, severe combined immunodeficiency, and propionic acidemia) are presented in detail as examples. RESULTS: Most states (88%) had Established Conditions lists to autoqualify children to EI. The average number of NBS conditions listed was 7.8 (range 0-34). Each condition appeared on average in 11.7 Established Conditions lists (range 2-29). After the literature review and consensus process, 29 conditions were likely to meet national criteria for an Established Condition. CONCLUSION: Despite benefiting from NBS and timely treatment, many children diagnosed with NBS conditions are at risk for developmental delays and significant medical complexity. The results demonstrate a need for more clarity and guidance regarding which children should qualify for EI. We suggest that most NBS conditions should automatically qualify based on the probability of resulting in a developmental delay. These findings suggest a future opportunity for collaboration between NBS and EI programs to create a consistent set of Established Conditions, potentially expediate referrals of eligible children, and streamline children's access to EI services.
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 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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".