Clinical phenotypes of Italian and Spanish patients with α<sub>1</sub>-antitrypsin deficiency
Bibliographic record
Abstract
With the aim of providing better clinical characterisation of patients with α1-antitrypsin deficiency (AATD), we analysed the data of adult patients with severe AATD enrolled in the Spanish and Italian national registries. We assessed 745 subjects, 416 of whom were enrolled in the Spanish registry and 329 in the Italian registry. 57.2% were male and 64.9% were smokers or former smokers with a mean±sd age of 49.9±13.8 years. Most (81.2%) were index cases, mainly having the PI*ZZ genotype (73.4%), and the mean±sd diagnostic delay was 9.0±12.1 years. Patients with chronic bronchitis were younger, had better preserved lung function and lower tobacco consumption. Overlap patients (chronic obstructive pulmonary disease with asthma) were mainly females, more frequently never-smokers and received respiratory medications more often. 48% of emphysema, 27.5% of chronic bronchitis and 44.8% of overlap subjects were receiving augmentation therapy. Compared with PI*ZZ patients (n=547), the PI*SZ (n=124) subjects were older at diagnosis and had more preserved lung function, despite a higher mean smoking consumption. Early diagnosis of AATD is still an unmet need. Augmentation therapy is administered to similar proportions of patients with different clinical phenotypes. PI*ZZ patients in both registries had more severe respiratory disease than those with PI*SZ, despite lower smoking levels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".