Incidence, risk factors and evolving treatment of severe retinopathy of prematurity in China: a retrospective multicenter cohort study in 70 neonatal intensive care units
Bibliographic record
Abstract
Purpose: To investigate the current incidence, risk factors and treatment for severe retinopathy of prematurity (ROP) in very preterm infants in China. Methods: This was a descriptive, retrospective cohort study. The study population was very preterm infants admitted to one of the 70 neonatal intensive care units (NICUs) in the Chinese Neonatal Network who underwent ROP screening between January 2019 and December 2020. The primary outcome was severe ROP, defined as type 1 or type 2 ROP. Potential risk factors were identified based on prior evidence and statistical significance. Adjusted odds ratios with 95% confidence intervals were calculated using a multivariable multinomial logistic regression model. Results: This study included 14,670 very preterm infants, among which 17.3% had low-grade ROP, and 8.5% had severe ROP. Treatment was performed in 1,381 (4.7%) eyes of the infants, among which 1,029 (74.5%), 148 (10.7%), and 124 (9.0%) received intravitreal anti-vascular endothelial growth factor injection only, laser treatment only, and vitreoretinal surgery, respectively. Extremely preterm birth (7.61, 95% CI: 6.12-9.46), small for gestational age (SGA; 2.31, 95% CI: 1.71-3.12), outborn status (1.43, 95% CI: 1.22-1.67), 1-min Apgar score < 7 (1.30, 95% CI: 1.11-1.51), >40 days on oxygen (1.70, 95% CI: 1.37-2.10), >28 days on parenteral nutrition (PN; 1.17, 95% CI: 1-1.35), and any inotropes use (2.12, 95% CI: 1.82-2.46) were associated with increased odds of severe ROP. Antenatal steroid use (0.77, 95% CI: 0.66-0.91), large for gestational age (LGA; 0.62, 95% CI: 0.45-0.85), and surfactant treatment (0.75, 95% CI: 0.64-0.87) were associated with reduced odds of severe ROP. Conclusion: The incidence of severe ROP in very preterm infants in China was high compared to the developed countries, and intravitreal anti-VEGF injection was the preferred treatment for severe ROP.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".