Alpha-1 antitrypsin deficiency: an update on clinical aspects of diagnosis and management
Bibliographic record
Abstract
Clinical heterogeneity has been demonstrated in alpha-1 antitrypsin deficiency (AATD), such that clinical suspicion plays an important role in its diagnosis. The PiZZ genotype is the most common severe deficiency genotype and so tends to result in the worst clinical presentation, hence it has been the major focus of research. However, milder genotypes, especially PiSZ and PiMZ, are also linked to the development of lung and liver disease, mainly when unhealthy behaviors are present, such as smoking and alcohol use. Monitoring and managing AATD patients remains an area of active research. Lung function tests or computed tomography (CT) densitometry may allow physicians to identify progressive disease during follow up of patients, with a view to decision making about AATD-specific therapy, like augmentation therapy, or eventually surgical procedures such as lung volume reduction or transplant. Different types of biological markers have been suggested for disease monitoring and therapy selection, although most need further investigation. Intravenous augmentation therapy reduces the progression of emphysema in PiZZ patients and is available in many European countries, but its effect in milder deficiency is less certain. AATD has also been suggested to represent a risk factor and trigger for pulmonary infections, like those induced by mycobacteria. We summarize the last 5-10 years' key findings in AATD diagnosis, assessment, and management, with a focus on milder deficiency variants.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".