Clinical and genetic characteristics of cystic fibrosis in CHINESE patients: a systemic review of reported cases
Bibliographic record
Abstract
Cystic fibrosis (CF) is a rare disease most commonly seen in Caucasians. Only a few Chinese CF patients have been described in literature, taking into account the large population of China. In this systematic review, we collected the clinical and genetic information of 71 Chinese CF patients based on all available data. Compared with Caucasians, Chinese CF patients often present atypical symptoms, mainly displaying symptoms of pulmonary infection with fewer digestive symptoms. An ethnicity-specific CFTR variant spectrum was also observed in CF patients of Chinese origin, with p.Gly970Asp as the most common mutation while p.Phe508del, the most common pathogenic mutation in CF patients of Caucasian origin, is rare, suggesting the necessity of a Chinese-specific CFTR variant screening panel. Besides, multiplex ligation-dependent probe amplification analysis should be routinely considered, especially for those with unidentified mutations. Potential under-diagnosis of CF in Chinese patients might be caused by a combination of atypical clinical features and genetic heterogeneity in Chinese CF patients, the inaccessibility of sweat and genetic testing facilities, and the one-child policy in China. With the approval of promising small molecule correctors and potentiators, molecular characterization of Chinese-specific CFTR mutations will help to realize more precise treatment for Chinese CF patients.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".