Introducing PELICAN: A new resource for understanding lung function trajectories in survivors of preterm birth
Bibliographic record
Abstract
Background: Existing cohort studies suggest infants born preterm (before 37 weeks’ gestation) are vulnerable to lung disease, which may be progressive over their lifespan. However, small sample sizes and high heterogeneity in individual studies have limited a thorough understanding of lung function trajectories, and the factors associated with both resiliant and adverse trajectories. We aimed to establish the world’s first harmonised global repository for lung health data from cohorts of survivors of preterm birth to generate greater statistical power and enable use of robust methods, expediting our understanding of lung health trajectories after preterm birth. Methods: PELICAN (Prematurity’s Effect on the Lungs In Children and Adults Network), was formed as a ERS Clinical Research Collaboration in 2020, with a work package to bring together data from multiple cohort studies. Datasets were identified following three systematic literature searches. A consensus-based variable selection and data harmonisation exercise was undertaken to determine data for inclusion. A secure, online data collection portal opened for contributions in 2022. Results: As of February 2025, the PELICAN repository contains data from 14 cohort studies, representing 10 countries and 2627 survivors of preterm birth (gestation range 22-37 weeks), including 952 with bronchopulmonary dysplasia. The PELICAN dataset has a mean (range) number of follow up visits of 1.8 (1-6). Conclusions: PELICAN presents a unique opportunity to fill key evidence gaps in the long-term respiratory outcomes of survivors of preterm birth, essential for guiding follow-up and early intervention for those at risk of poorer outcomes.
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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 | 0.017 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.111 | 0.025 |
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".