Most costly and prevalent reasons for hospitalization in children with medical complexity in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Children with medical complexity (CMC) have chronic health conditions often associated with functional limitations. CMC comprise 1%-5% of the pediatric population. In Canada, their care accounts for one-third of pediatric health spending. We aim to describe the most costly and prevalent conditions leading to hospitalization in CMC in Ontario, Canada. METHODS: Population-based, cross-sectional study from a universally funded system utilizing health administrative databases. Children (<18 years old) with valid provincial healthcare coverage admitted to a hospital from 2014 to 2019 were included. CMC was defined using validated algorithms. Encounters were classified into clinical conditions using the Pediatric Clinical Classification System. Outcomes included condition-specific prevalence, cost, and cost rank estimated using a costing algorithm in Canadian dollars. RESULTS: There were 627,314 pediatric hospitalizations, costing $4.28 billion. Of these, 141,653 (23%) hospitalizations were for CMC, costing $2.25 billion (52%). Among encounters for CMC, 84,280 (60%) were for children with medical technology. One-third of hospitalizations in CMC were in community hospitals. Nearly half (1.30 million, 46%) of days in hospital were in CMC, along with 60% of intensive care unit (ICU) days (667,497 days). Low birth weight ($555.4 million), prematurity ($70.0 million), and respiratory distress of the newborn ($46.6 million) were the costliest conditions. Low birth weight (88 per 1000 encounters), chemotherapy (42 per 1000 encounters), and pneumonia (29 per 1000 encounters) were the most prevalent conditions. CONCLUSIONS: Understanding the most costly and prevalent inpatient conditions in CMC will help to prioritize more targeted research questions and interventions to improve healthcare utilization and patient 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".