Lifetime patient outcomes and healthcare utilization for Bronchopulmonary dysplasia (BPD) and extreme preterm infants: a microsimulation study
Bibliographic record
Abstract
BACKGROUND: Bronchopulmonary dysplasia (BPD) is among the most severe chronic lung diseases and predominantly affects premature infants. There is a general understanding of BPD's significant impact on the short-term outcomes however there is little evidence on long-term outcomes. Our study estimates the lifetime clinical outcomes, quality of life, and healthcare costs of BPD and associated complications. METHODS: We developed a microsimulation model to estimate lifetime clinical and economic burden of BPD among extreme preterm infants (≤28 weeks gestational age at birth) and validated it against the best available Canadian data. We further estimate the cumulative incidence of major complications associated with BPD, differentiated by BPD severity and gestational age category. RESULTS: We find, on average, patients with BPD and resulting complications will incur over CAD$700,000 in lifetime health systems costs. We also find the average life expectancy of BPD patients to be moderately less than that of the general population and significant reductions in quality-adjusted life year due to major complications. Healthcare utilization and quality of life measures vary dramatically according to BPD severity, suggesting significant therapeutic headroom for interventions that can prevent or mitigate the effects of BPD for patients. CONCLUSIONS: Our study adds a significant expansion of existing evidence by presenting the lifetime burden of BPD based on key patient characteristics. Given the extreme cost burden at the earliest stage of life and lifetime negative impact on quality of life, there is larger headroom for investment in prevention and mitigation of severe BPD than is currently available.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".