Genetic epidemiology of alpha‐1 antitrypsin deficiency in North America and Australia/New Zealand: Australia, Canada, New Zealand and the United States of America
Bibliographic record
Abstract
Alpha-1-antitrypsin deficiency (AAT deficiency) is one of the most common serious hereditary disorders in the world, as its affects all major racial subgroups worldwide, and there are an estimated 120.5 million carriers and deficient subjects worldwide. This genetic disease is related to susceptibility for development of jaundice in infants, liver disease in children and adults and pulmonary emphysema in adults. Moreover, AAT deficiency carrier phenotypes (PiMS and PiMZ) and deficiency allele phenotypes (PiSS, PiSZ and PiZZ) are suspected to predispose subjects to a variety of other adverse health effects. Because there is a limited database on the number of individuals affected by this disease worldwide, we have collected data on control cohorts in genetic epidemiological studies published on case-control studies in the peer-reviewed literature worldwide. Based on these data, we estimated the numbers of carriers and deficiency allele combinations for the two most common defective alleles, namely PiS and PiZ in 58 countries worldwide. The present paper focuses on the distribution of the PiS and PiZ deficiency alleles in Australia, Canada, New Zealand and the United States of America. A total of 31,042,232 individuals at risk for adverse health effects have been calculated in these four countries: 2,144,158 in Australia, 3,258,564 in Canada, 430,922 in New Zealand and 24,909,548 in the United States of America. The prevalences for all five phenotypic classes of AAT deficiency in each of these countries is as follows: Australia 1 out of 8.9, Canada 1 out of 9.8, New Zealand 1 out of 8.5 and the United States of America 1 out of 11.3. The geographical distribution of individual control cohorts and estimates of the numbers of carriers and deficiency allele phenotypes in each of these four countries are given in individual tables.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".