Universal screening of newborns for biliary atresia: Cost-effectiveness of alternative strategies
Bibliographic record
Abstract
Objective Biliary atresia, a rare newborn liver disease, is the most common cause of liver-related death in children and the main indication for paediatric liver transplantation. Early detection and surgical intervention with a Kasai portoenterostomy offers the best chance for long-term patient survival. We conducted a cost-effectiveness analysis to compare no universal screening with screening using either a home-based infant stool colour card with passive card distribution strategy, or conjugated bilirubin testing. Methods A Markov model was developed, with structure, costs, and probabilities informed by the literature and clinical expert opinion, to simulate a newborn cohort over a 10-year time horizon. Health benefits were expressed as life-years gained. This analysis was conducted from the perspective of the Canadian publicly funded health care system (all costs in Canadian dollars). Both deterministic and probabilistic analyses were conducted. Results Screening using a home-based colour card with passive card distribution was a cost-effective option. For a population of 392,902 annual births in Canada, this strategy cost approximately $192,000 more than no universal screening but led to eight life-years gained (incremental cost-effectiveness ratio (ICER) = $24,065 per life-year gained). Screening using conjugated bilirubin testing versus the colour card cost $2,369,199 more and led to five more life-years gained (ICER= $473,840 per life year gained), and so was not cost-effective. Conclusions A home-based screening program using infant stool colour cards with a passive distribution strategy could be highly cost-effective when administered at a low unit cost and with a reasonable screening performance.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".