The Current State of Neonatal Neurodevelopmental Follow-up Programs in North America: A Children's Hospitals Neonatal Consortium Report
Bibliographic record
Abstract
OBJECTIVE: This study aimed to determine neonatal neurodevelopmental follow-up (NDFU) practices across academic centers. STUDY DESIGN: This study was a cross-sectional survey that addressed center-specific neonatal NDFU practices within the Children's Hospitals Neonatal Consortium (CHNC). RESULTS: Survey response rate was 76%, and 97% of respondents had a formal NDFU program. Programs were commonly staffed by neonatologists (80%), physical therapists (77%), and nurse practitioners (74%). Median gestational age at birth identified for follow-up was ≤32 weeks (range 26-36). Median duration was 3 years (range 2-18). Ninety-seven percent of sites used Bayley Scales of Infant and Toddler Development, but instruments used varied across ages. Scores were recorded in discrete electronic data fields at 43% of sites. Social determinants of health data were collected by 63%. Care coordination and telehealth services were not universally available. CONCLUSION: NDFU clinics are almost universal within CHNC centers. Commonalities and variances in practice highlight opportunities for data sharing and development of best practices. KEY POINTS: · Neonatal NDFU clinics help transition high-risk infants home.. · Interdisciplinary neonatal intensive care unit follow-up brings together previously separated outpatient service lines.. · This study reviews the current state of neonatal NDFU in North America..
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".