<i>CFTR</i> Mutations in Turkish and North African Cystic Fibrosis Patients in Europe: Implications for Screening
Bibliographic record
Abstract
AIMS: To obtain more insight into the variability of the CFTR mutations found in immigrant cystic fibrosis (CF) patients who are living in Europe now, and to estimate the test sensitivity of different frequently used methods of DNA analysis to detect CF carriers or patients among these Turkish or North African immigrants. METHODS: A survey among 373 European CF centers asking which CFTR mutations had been found in Turkish and North African CF patients. RESULTS: 31 and 26 different mutations were reported in Turkish and North African patients, identifying 64.2% (113/176) and 87.4% (118/135) alleles, respectively (p < 0.001). The mean sensitivity (detection rate) of three most common CFTR mutation panels to detect these mutations differed between Turkish and North African people, 44.9% (79/176) versus 69.6% (94/135) (p < 0.001), and can be increased to 57.4% (101/176) and 79.3% (107/135) (p < 0.001), respectively, by expanding these panels with 13 mutations which have been found on two or more alleles. CONCLUSION: 35.8% and 12.6%, respectively, of CF alleles in Turkish and North African patients living in Europe now had not been identified. Among these populations, the test sensitivity of common CFTR mutation panels is insufficient for use in screening programs in Europe, even after expansion with frequent Turkish and North African mutations. This raises questions about whether and how to implement CF carrier and neonatal screening in a multiethnic society.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".