Sex-Differences in Alpha-1 Antitrypsin Deficiency: Data From the EARCO Registry
Bibliographic record
Abstract
Sex and gender influence many aspects of chronic obstructive pulmonary disease (COPD). Limited data are available on this topic in alpha-1 antitrypsin deficiency (AATD). We therefore aimed to investigate sex issues in the EARCO registry, a prospective, international, observational cohort study. Baseline data from PiZZ individuals, enrolled in the registry with complete data on sex and smoking history were analysed by group comparisons and binary logistic regression analyses. 1283 patients with AATD, 49.3% women were analysed. Females reported less tobacco consumption (16.8 ± 12.2 vs. 19.6 ± 14.5 PY, p = 0.006), occupational exposures towards gases, dusts or asbestos (p < 0.005 each) and consumed less alcohol (5.5 ± 7.6 vs. 8.4 ± 10.3 u/week, p < 0.001). Females reported COPD (41% vs. 57%, p < 0.001) and liver disease (11% vs. 20%, p < 0.001) less often. However, they had a higher prevalence of bronchiectasis (24% vs. 13%, p < 0.001). Despite better lung function (FEV1%pred. 73.6 ± 29.9 vs. 62.7 ± 29.5, p < 0.001) females reported a similar symptom burden (CAT 13.4 ± 9.5 vs. 12.5 ± 8.9, p = ns) and exacerbation frequency (at least one in the previous year 30% vs. 26%, p = ns) compared to males. In multivariate analyses, female sex was an independent risk factor for exacerbations in the previous year OR 1.6 p = 0.001 in addition to smoking history, COPD, asthma and bronchiectasis and was also identified as risk factors for symptom burden (CAT ≥ 10) OR 1.4 p = 0.014 besides age, BMI, COPD and smoking history. Men had higher rates of COPD and liver disease, women were more likely to have bronchiectasis. Women's higher symptom burden and exacerbation frequency suggest they may need tailored treatment approaches.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".