Newer bronchopulmonary dysplasia definitions and prediction of health economics impacts in very preterm infants
Bibliographic record
Abstract
OBJECTIVE: To compare the abilities of bronchopulmonary dysplasia (BPD) definitions to predict hospital charges as a surrogate of disease complexity. METHODS: Retrospective study of infants admitted to the neonatal intensive care unit (NICU) less than 32 weeks gestational age. Subjects were classified according to the Canadian Neonatal Network (CNN), the National Institute of Child Health and Human Development (NICHD) (2018), and Jensen BPD definitions as none, mild (1), moderate (2), or severe (3) BPD. Spearman's correlation was performed to evaluate the association of BPD definitions with health economics outcomes. RESULTS: One hundred and sixty-eight infants were included with mean birth weight of 1197 g and mean gestational age of 28.4 weeks. More infants were classified as no BPD according to CNN definition (79%) in comparison to NICHD 2018 (64.3%) and Jensen (59.5%) definitions. There were fewer infants as the grade of severity increased for all definitions, this was most linear for Jensen definition with Grade 1 present in 25%, Grade 2 in 12.5%, and Grade 3 in 3%. A stronger correlation with NICU length of stay, NICU hospital charges, NICU charges per day, and first year of life hospital charges was detected for Jensen definition (correlation coefficient of 0.58, 0.66, 0.64, 0.67, respectively) in comparison to CNN and NICHD 2018 definitions (p < .0001). CONCLUSION: Jensen BPD definition had the strongest correlation with first year health economics outcomes in our study. Validating recent BPD definitions using population-based data is imperative to improve family counseling and enhance the designs of quality improvement initiatives and therapeutic research studies targeting patient-centric 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".