Chinese Neonatal Follow-Up Network: a national protocol for follow-up assessment and collaborative research to improve developmental outcomes of high-risk preterm infants
Bibliographic record
Abstract
INTRODUCTION: The objective of the Chinese Neonatal Follow-Up Network (CHNFUN) is to establish a standardised follow-up protocol for the assessment of high-risk preterm infants, and collaborative research aimed at improving early intervention and neurodevelopmental outcomes for preterm infants with gestational age less than 32 weeks in China. The CHNFUN is the first national neonatal follow-up network and has the largest geographically representative cohort from neonatal intensive care units (NICUs) in China. METHODS AND ANALYSIS: A survey of neonatal follow-up clinics participating in CHNFUN was used to inform the development of a standardised protocol for the assessment of high-risk preterm infants in China. Training in the use of assessment tools and data collection was provided to all participating centres. Individual-level neurodevelopmental outcomes data from participating neonatal follow-up clinics will be collected at corrected age, 40 weeks, 3-4 months, 12 months, 18-24 months, 3 years and 6 years of age, using a unique database developed by the CHNFUN and linked to NICU outcomes data in the CHNN Database. Data will be prospectively collected on an ongoing basis from all surviving infants born at <32 weeks' gestation or <1500 g birth weight and discharged from 34 participating NICUs from 1 June 2023. Infant neurodevelopmental outcomes and interinstitutional variations in outcomes will be examined and used to inform quality improvement measures aimed at improving outcomes, development and evaluation of early intervention programmes and other collaborative research, including clinical trials. ETHICS AND DISSEMINATION: This study was approved by the ethics review board of Children's Hospital of Fudan University (#CHFU 2022-112), which was recognised by all participating hospitals. Waiver of consent was granted at all sites. Only non-identifiable patient-level data will be transmitted, and only aggregate data will be reported in CHNFUN reports and publications.
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 | 0.106 | 0.074 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.005 |
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".