COPD phenotypes in biomass smoke-<i>versus</i>tobacco smoke-exposed Mexican women
Bibliographic record
Abstract
We hypothesised that biomass smoke exposure is associated with an airway-predominant chronic obstructive pulmonary disease (COPD) phenotype, while tobacco-related COPD is associated with an emphysema-predominant phenotype. In this cross-sectional study, female never-smokers with COPD and biomass exposure (n=21) and female ex-cigarette smokers with COPD without biomass exposure (n=22) completed computed tomography (CT) at inspiration and expiration, pulmonary function, blood gas, exercise tolerance, and quality of life measures. Two radiologists scored the extent of emphysema and air trapping on CT. Quantitative emphysema severity and distribution and airway wall thickness were calculated using specialised software. Women in the tobacco group had significantly more emphysema than the biomass group (radiologist score 2.3 versus 0.7, p=0.001; emphysema on CT 27% versus 19%, p=0.046; and a larger size of emphysematous spaces, p=0.006). Women in the biomass group had significantly more air trapping than the tobacco group (radiologist score 2.6 and 1.5, respectively; p=0.02) and also scored lower on the symptom, activities and confidence domains of the quality of life assessment and had lower oxygen saturation at rest and during exercise (p<0.05). Biomass smoke exposure is associated with less emphysema but more air trapping than tobacco smoke exposure, suggesting an airway-predominant phenotype.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".