Assessing the Potential Success of Cystic Fibrosis Carrier Screening: Lessons Learned from Tay-Sachs Disease and β-Thalassemia
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to identify factors involved in the success of 2 well-established population-based carrier screening programs - Tay-Sachs disease (TSD) in Ashkenazi Jews and beta-thalassemia in Sardinia and Cyprus - and to assess the potential for success of a population-based cystic fibrosis (CF) carrier screening strategy using these factors. METHODS: We performed a literature review and key informant interviews. RESULTS: Factors involved in the success of TSD and beta-thalassemia carrier screening programs include disease characteristics (well-defined population at risk, severe disease with predictable course, availability of effective treatment), test characteristics (high sensitivity, straightforward interpretation of results), and community characteristics (involvement of community, support of families and advocacy groups, consensus in favor of avoiding affected births). Current CF screening strategies include few of the factors listed above. Unlike TSD and beta-thalassemia, the purpose of current CF carrier screening strategies is informed reproductive decision-making, without an explicit goal of reducing disease incidence. CONCLUSION: When compared to TSD and beta-thalassemia, CF is a less favorable candidate for population-based carrier screening. Because of its different purpose, CF carrier screening will require different measures of success than those used for TSD and beta-thalassemia carrier screening, and a consensus on the value or success of CF carrier screening may be difficult to achieve.
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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 | 0.043 | 0.110 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".