α1-Antitrypsin Deficiency Alleles in Cystic Fibrosis Lung Disease
Bibliographic record
Abstract
Cystic fibrosis (CF) transmembrane conductance regulator (CFTR) genotype does not explain the heterogeneity observed in CF pulmonary disease severity. Modifier genes are implicated for this heterogeneity. alpha1-antitrypsin (alpha1-AT) is one of the few antiproteases capable of inactivating neutrophil elastase. We investigated whether alpha1-AT alleles (Z, S deficiency alleles and the 3' G1237-->A mutation) were associated with increased disease severity and the alpha1-AT acute phase response during pulmonary exacerbations. This was a multicenter Canadian study. Seven hundred sixteen patients with CF (age range, 5.0-63.6 yr) were genotyped for the Z, S, and G1237-->A polymorphisms of the alpha1-AT gene. Stable and acute levels of alpha1-AT were measured on 31 adult patients with CF and were correlated to clinical parameters. There were 69, 13, and 18 patients with CF who were MS, SS, and MZ, respectively. There were 95 and 7 patients with CF heterozygous or homozygous for the A1237 allele, respectively. alpha1-AT genotype did not predict pulmonary disease severity, and was not associated with more severe clinical outcome (death or lung transplantation) or age of onset of Pseudomonas aeruginosa infection. Body mass index was a significant predictor of alpha1-AT levels during exacerbations. alpha1-AT genotype is not a major contributor to the variability of pulmonary disease severity in CF.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".