Sex and gender in bronchiectasis: an analysis of two Italian centres and a review of literature
Bibliographic record
Abstract
Non-cystic fibrosis bronchiectasis presents a higher prevalence among females in most countries and women affected by bronchiectasis have a more severe disease course and a worse outcome. Some mechanisms suggested to be implied in this gender disparity are anatomical predisposition and differences in the respiratory microbiome. Moreover, some of the conditions that cause bronchiectasis are more common in females. There is increasing evidence regarding the role of sex specific hormones: in vitro studies have shown how progesterone reduces mucociliary clearance, whereas oestradiol was seen to be associated with mucoid conversion and increased exacerbations in patients colonised with Pseudomonas aeruginosa. Our analysis considered 201 patients diagnosed with non-cystic fibrosis bronchiectasis, of which 170 referred to the Respiratory Unit of San Matteo Hospital of Pavia and 31 to the Respiratory Unit of Ospedali Riuniti of Foggia. According to literature, the majority of these patients were females (78.6%). We found that a greater percentage of females had at least one colonization (whether bacterial, fungal or mycobacterial) compared to males (p=0.028), women had lower BMI (p=0.005) and higher frequency of pneumonia (p<0.001). On the other hand, despite the fact that most patients were females we didn’t observe a statistically significant difference regarding age, number of exacerbations, number of bacterial, fungal, viral or NTM isolates, symptoms and disease severity, assessed by the BSI (Bronchiectasis Severity Index), between males and females. This might find explanation in the small number of patients taken into consideration, thus requiring further analysis and data collection.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".