Status of the neonatal follow-up system in China: survey and analysis
Bibliographic record
Abstract
BACKGROUND: There is little information about neonatal follow-up programs (NFUPs) in China. This study aimed to conduct a survey of hospitals participating in the Chinese Neonatal Network (CHNN) to determine the status of NFUPs, including resources available, criteria for enrollment, neurodevelopmental assessments, and duration of follow-up. METHODS: We conducted a descriptive study using an online survey of all 72 hospitals participating in CHNN in 2020. The survey included 15 questions that were developed based on the current literature and investigators' knowledge about follow-up practices in China. RESULTS: Sixty-four (89%) of the 72 hospitals responded to the survey, with an even distribution of children's (31%), maternity (33%) and general (36%) hospitals. All but one (98%) hospital had NFUPs, with 44 (70%) being established after 2010. Eligibility criteria for follow-up were variable, but common criteria included very preterm infants < 32 weeks or < 2000 g birth weight (100%), small for gestational age (97%), hypoxic ischemic encephalopathy (98%) and postsurgery (90%). The average follow-up rate was 70% (range: 7.5%-100%). Only 12% of hospitals followed up with patients for more than 24 months. There was significant variation in neurodevelopmental assessments, follow-up schedule, composition of staff, and clinic facilities and resources. None of the staff had received formal training, and only four hospitals had sent staff to foreign hospitals as observers. CONCLUSIONS: There is significant variation in eligibility criteria, duration of follow-up, types of assessments, staffing, training and facilities available. Coordination and standardization are urgently needed.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| 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".