Dépistage néonatal de l’hypothyroïdie congénitale et de l’hyperplasie congénitale des surrénales
Bibliographic record
Abstract
Newborn screening is an important public health program and a triumph of preventive medicine. Economic analyses show that the benefits of newborn screening clearly outweigh the costs for certain diseases, but not necessarily for other ones. This is due to the great diversity of the natural history of the diseases detected, to the fact that each of these diseases considered individually is rare, and to differences in the effectiveness of interventions. In addition, the benefit-cost ratio of screening for a particular disorder may differ between countries, specifically between high-income and low- and middle-income countries. The burden of a disorder may also be alleviated by increased clinical awareness and effective clinical services, even in the absence of newborn screening. In this article, we focus on economic analyses of newborn screening for primary congenital hypothyroidism, which has been in place in high-income countries for roughly 40 years, and for classic congenital adrenal hyperplasia due to 21-hydroxylase deficiency. Screening for the latter is not yet universal, even in high-income countries, although the lack of universal implementation may reflect factors other than economic considerations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".