Augmentation Therapy for α<sub>1</sub>Antitrypsin Deficiency: A Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Augmentation with exogenous alpha1-antitrypsin (alpha1-AT) is the only specific therapy for alpha1-AT deficiency. Uncertainty persists concerning its effectiveness. PURPOSE: To test the hypothesis that augmentation therapy in patients with alpha1-AT deficiency slows the decline in FEV1. STUDY SELECTION: Randomized and nonrandomized clinical studies with either parallel-group design or single cohort pre-post design were eligible if they compared augmentation therapy with a control regimen and if long-term (> 1 y) longitudinal FEV1 follow-up data were collected. DATA SYNTHESIS: FEV1 data from five trials with 1509 patients were combined by random effects meta-analysis. The decline in FEV1 was slower by 23% (absolute difference, 13.4 ml/year; CI, 1.5 to 25.3 ml/year) among all patients receiving augmentation therapy. This overall protective effect reflected predominantly the results in the subset of patients with baseline FEV1 30-65% of predicted. In that subset, augmentation was associated with a 26% reduction in rate of FEV1 decline (absolute difference, 17.9 ml/year; CI, 9.6 to 26.1 ml/year). Similar trends amongst patients with baseline FEV1 percent of predicted < 30% or > 65% were not statistically significant. CONCLUSIONS: This meta-analysis supports the conclusion that augmentation can slow lung function decline in patients with AAT deficiency Patients with moderate obstruction are most likely to benefit.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.049 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".