Challenges in Assessing the Cost-Effectiveness of Newborn Screening: The Example of Congenital Adrenal Hyperplasia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Generalizing about the cost-effectiveness of newborn screening (NBS) is difficult due to the heterogeneity of disorders included in NBS panels, along with data limitations. Furthermore, it is unclear to what extent evidence about cost-effectiveness should influence decisions to screen for specific disorders. Screening newborns for congenital adrenal hyperplasia (CAH) due to 21-hydroxylase deficiency can serve as a useful test case, since there is no global consensus on whether CAH should be part of NBS panels. Published and unpublished cost-effectiveness analyses of CAH screening have yielded mixed findings, largely due to differences in methods and data sources for estimating health outcomes and associated costs of early versus late diagnosis as well as between-country differences. Understanding these methodological challenges can help inform future analyses and could also help interested policymakers interpret the results of economic evaluations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it