Very Preterm Infants with Technological Dependence at Home: Impact on Resource Use and Family
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of medical complexity among very preterm infants on health care resource use, family, and neurodevelopmental outcomes at 18 months' corrected age. METHODS: This observational cohort study of Canadian infants born < 29 weeks' gestational age in 2009-2011 compared infants with and those without medical complexity defined as discharged home with assistive medical technology. Health care resource use and family outcomes were collected. Children were assessed for cerebral palsy, deafness, blindness, and developmental delay at 18 months. Logistic regression analysis was performed for group comparisons. RESULTS: Overall, 466/2,337 infants (20%) needed assistive medical technology at home including oxygen (79%), gavage feeding (21%), gastrostomy or ileostomy (20%), CPAP (5%), and tracheostomy (3%). Children with medical complexity were more likely to be re-hospitalized (OR 3.6, 95% CI 3.0-4.5) and to require ≥2 outpatient services (OR 4.4, 95% CI 3.5-5.6). Employment of both parents at 18 months was also less frequent in those with medical complexity compared to those without medical complexity (52 vs. 60%, p < 0.01). Thirty percent of children with medical complexity had significant neurodevelopmental impairment compared to 13% of those without medical complexity (p < 0.01). Lower gestational age, lower birth weight, bronchopulmonary dysplasia, sepsis, and surgical necrotizing enterocolitis were associated with a risk of medical complexity. CONCLUSION: Medical complexity is common following very preterm birth and has a significant impact on health care use as well as family employment and is more often associated with neurodevelopmental disabilities. Efforts should be deployed to facilitate care coordination upon hospital discharge and to support families of preterm children with medical complexity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".