Impact of Bronchopulmonary Dysplasia, Brain Injury, and Severe Retinopathy on the Outcome of Extremely Low-Birth-Weight Infants at 18 Months
Bibliographic record
Abstract
CONTEXT: Despite more than 2 decades of outcomes research after very preterm birth, clinicians remain uncertain about the extent to which neonatal morbidities predict poor long-term outcomes of extremely low-birth-weight (ELBW) infants. OBJECTIVE: To determine the individual and combined prognostic effects of bronchopulmonary dysplasia (BPD), ultrasonographic signs of brain injury, and severe retinopathy of prematurity (ROP) on 18-month outcomes of ELBW infants. DESIGN: Inception cohort assembled for the Trial of Indomethacin Prophylaxis in Preterms (TIPP). SETTING AND PARTICIPANTS: A total of 910 infants with birth weights of 500 to 999 g who were admitted to 1 of 32 neonatal intensive care units in Canada, the United States, Australia, New Zealand, and Hong Kong between 1996 and 1998 and who survived to a postmenstrual age of 36 weeks. MAIN OUTCOME MEASURES: Combined end point of death or survival to 18 months with 1 or more of cerebral palsy, cognitive delay, severe hearing loss, and bilateral blindness. RESULTS: Each of the neonatal morbidities was similarly and independently correlated with a poor 18-month outcome. Odds ratios were 2.4 (95% confidence interval [CI], 1.8-3.2) for BPD, 3.7 (95% CI, 2.6-5.3) for brain injury, and 3.1 (95% CI, 1.9-5.0) for severe ROP. In children who were free of BPD, brain injury, and severe ROP the rate of poor long-term outcomes was 18% (95% CI, 14%-22%). Corresponding rates with any 1, any 2, and all 3 neonatal morbidities were 42% (95% CI, 37%-47%), 62% (95% CI, 53%-70%), and 88% (64%-99%), respectively. CONCLUSION: In ELBW infants who survive to a postmenstrual age of 36 weeks, a simple count of 3 common neonatal morbidities strongly predicts the risk of later death or neurosensory impairment.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".