The prevalence and clinical characteristics of cystic fibrosis in South Asian Canadian immigrants
Bibliographic record
Abstract
BACKGROUND: Cystic fibrosis (CF) is considered to be rare among individuals from the Indian subcontinent. Furthermore, affected individuals are reported to experience a more severe clinical course. AIMS: It was hypothesised that CF is under diagnosed in people of South Asian origin and therefore the prevalence may be higher than previously estimated. METHODS: The prevalence of CF in the South Asian and in the general population living in the same geographic region (Metropolitan Toronto) were compared between 1996 and 2001. Population data were obtained from the Canadian census survey. CF phenotype and genotype data were obtained from the Toronto CF database. RESULTS: Among 381 patients with CF, 15 were of South Asian descent. The age related prevalence of CF among the South Asian and general populations was: 0-14 years, 1:9200 versus 1:6600; 15-24 years, 1:13,200 versus 1:7600; older than 25 years, 1:56,600 versus 1:12,400. Age at diagnosis, duration and severity of symptoms at diagnosis, current nutritional status, and FEV(1) were similar in the two groups. While not significant, FEV1 tended to be lower (48% versus 57% predicted) among adult South Asians, compared to the general CF population. Also, the percentage with pancreatic sufficiency was higher (27% versus 16%) and the frequency of DeltaF508 allele was lower (50% versus 65.1%). CONCLUSIONS: These data suggest that the prevalence and natural history of CF in South Asians is similar to that among individuals of European origin. The relatively lower prevalence among older South Asians may reflect an improving recognition of CF in this ethnic subgroup.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".