Carrier screening programs for rare diseases in developed countries and the case of Turkey: A systematic review
Bibliographic record
Abstract
Effective control of rare diseases requires health programs based on principles of protection and prevention. Carrier screening programs serve as preventive measures by identifying at-risk groups. This review examines the impact, implementation, advantages, and disadvantages of carrier screening, incorporating examples from ten countries: the United States, Canada, the United Kingdom, Israel, China, Australia, Italy, Germany, the Netherlands, and Turkey. Data on carrier screening and related policies were collected from July to November 2022 and presented in a tabular format using a coding system devised by the authors. Variability was observed in the diseases/disorders and populations screened, screening expenses, and government provision across the countries. The number of diseases/disorders examined, ranging from 3 to 47, was determined by committee guidelines, government resources, pilot studies, and national institute resources. Notably, carrier screening programs exhibited greater worldwide inconsistency compared to newborn screening programs. The comparative analysis of developed countries serves to guide emerging nations. To address inequalities at both local and global levels, there is a need to enhance the establishment, development, and implementation of carrier screening programs. Furthermore, cost analyses of screening should be conducted, and adequate funding should be allocated to countries. In conclusion, this review highlights the preventive potential of carrier screening for rare diseases and emphasizes the importance of improving carrier screening programs globally to achieve equitable healthcare outcomes.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".