A Decision-Tree Approach to Cost Comparison of Newborn Screening Strategies for Cystic Fibrosis
Bibliographic record
Abstract
OBJECTIVE: Because cystic fibrosis can be difficult to diagnose and treat early, newborn screening programs have rapidly developed nationwide but methods vary widely. We therefore investigated the costs and consequences or specific outcomes of the 2 most commonly used methods. METHODS: With available data on screening and follow-up, we used a simulation approach with decision trees to compare immunoreactive trypsinogen (IRT) screening followed by a second IRT test against an IRT/DNA analysis. By using a Monte Carlo simulation program, variation in the model parameters for counts at various nodes of the decision trees, as well as for costs, are included and applied to fictional cohorts of 100 000 newborns. The outcome measures included the numbers of newborns given a diagnosis of cystic fibrosis and costs of screening strategy at each branch and cost per newborn. RESULTS: Simulations revealed a substantial number of potential missed diagnoses for the IRT/IRT system versus IRT/DNA. Although the IRT/IRT strategy with commonly used cutoff values offers an average overall cost savings of $2.30 per newborn, a breakdown of costs by societal segments demonstrated higher out-of-pocket costs for families. Two potential system failures causing delayed diagnoses were identified relating to the screening protocols and the follow-up system. CONCLUSIONS: The IRT/IRT screening algorithm reduces the costs to laboratories and insurance companies but has more system failures. IRT/DNA offers other advantages, including fewer delayed diagnoses and lower out-of-pocket costs to families.
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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 | 0.021 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".