Cost-effectiveness analysis of a system-based approach for managing neonatal jaundice and preventing kernicterus in Ontario
Bibliographic record
Abstract
OBJECTIVE: To evaluate the incremental cost-effectiveness of a system-based approach for the management of neonatal jaundice and the prevention of kernicterus in term and late-preterm (≥35 weeks) infants, compared with the traditional practice based on visual inspection and selected bilirubin testing. STUDY DESIGN: Two hypothetical cohorts of 150,000 term and late-preterm neonates were used to compare the costs and outcomes associated with the use of a system-based or traditional practice approach. Data for the evaluation were obtained from the case costing centre at a large teaching hospital in Ontario, supplemented by data from the literature. RESULTS: The per child cost for the system-based approach cohort was $176, compared with $173 in the traditional practice cohort. The higher cost associated with the system-based cohort reflects increased costs for predischarge screening and treatment and increased postdischarge follow-up visits. These costs are partially offset by reduced costs from fewer emergency room visits, hospital readmissions and kernicterus cases. Compared with the traditional approach, the cost to prevent one kernicterus case using the system-based approach was $570,496, the cost per life year gained was $26,279, and the cost per quality-adjusted life year gained was $65,698. CONCLUSION: The cost to prevent one kernicterus case using the system-based approach is much lower than previously reported in the literature.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".