Beyond Bronchopulmonary Dysplasia: A Comprehensive Review of Chronic Lung Diseases in Neonates
Bibliographic record
Abstract
In neonates, pulmonary diseases such as bronchopulmonary dysplasia and other chronic lung diseases (CLDs) pose significant challenges due to their complexity and high degree of morbidity and mortality. This review discusses the etiology, pathophysiology, clinical presentation, and diagnostic criteria for these conditions, as well as current management strategies. The review also highlights recent advancements in understanding the pathophysiology of these diseases and evolving strategies for their management, including gene therapy and stem cell treatments. We emphasize how supportive care is useful in managing these diseases and underscore the importance of a multidisciplinary approach. Notably, we discuss the emerging role of personalized medicine, enabled by advances in genomics and precision therapeutics, in tailoring therapy according to an individual's genetic, biochemical, and lifestyle factors. We conclude with a discussion on future directions in research and treatment, emphasizing the importance of furthering our understanding of these conditions, improving diagnostic criteria, and exploring targeted treatment modalities. The review underscores the need for multicentric and longitudinal studies to improve preventative strategies and better understand long-term outcomes. Ultimately, a comprehensive, innovative, and patient-centered approach can enhance the quality of care and outcomes for neonates with CLDs.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".