Risk Factors for Severe Respiratory Morbidity at 2 Years of Life in Children Born Extremely Preterm With Bronchopulmonary Dysplasia
Bibliographic record
Abstract
Abstract Background: Bronchopulmonary dysplasia (BPD), defined as need for oxygen /respiratory support at 36 weeks gestational age (GA), affects 55% of children born < 29 weeks, who have increased risk of post-prematurity respiratory disease (PRD), defined as: ≥ 2 respiratory-related hospitalizations, home oxygen/respiratory support at 3 months, systemic corticosteroids/pulmonary vasodilators post-discharge, death from cardiopulmonary cause. We hypothesize that BPD, higher pCO2, and pulmonary hypertension (PH) before NICU discharge will predict PRD. Objectives: 1) Identify clinical factors prior to NICU discharge associated with PRD by 2 years; 2) Identify clinical factors associated with emergency room (ER) visits by 2 years; 3) Compare predictive performance of individual predictors and multivariable model for PRD. Methodology: Children born at <29 weeks GA with ≥1 echocardiogram before NICU discharge at two tertiary centres were included. Chart review included clinical factors at NICU discharge, ER visits, and respiratory-related hospitalizations by 2 years. Analysis of predictors included logistic regression and ROC analysis. Results: 53 infants (42%) had BPD. 24 (19%) experienced PRD. All who experienced PRD had BPD. Children with BPD had 2.6 times as many ER visits as those without. More severe BPD (OR: 120; 95% CI: 15.8,15467), but not high pCO2 or PH, were associated with PRD. On ROC analysis, combination of BPD severity, pCO2 and PH demonstrated sensitivity of 50% and specificity of 97% for prediction of PRD. Conclusion: The combination of BPD severity, pCO2, and PH best predicted PRD. Identifying extremely preterm infants at high risk of developing PRD can guide counseling of families.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".