Effect of obesity on airway inflammation: a cross‐sectional analysis of body mass index and sputum cell counts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Several observational studies have demonstrated an association between obesity and asthma. Studies evaluating exhaled nitric oxide levels and obesity have revealed that a higher body mass index (BMI) is associated with elevated exhaled nitric oxide levels. Airway inflammation using sputum cell counts has not been assessed in obese patients with airway diseases. OBJECTIVE: The primary aim of this study was to determine whether obesity (based on BMI) is associated with eosinophilic or neutrophilic bronchitis. METHODS: The results from a database of induced sputum cell counts were compared with BMI and analysed using correlation statistics, regression and parametric and non-parametric analysis. RESULTS: Seven-hundred and twenty-seven adult participants with an equal number of sputum samples were included in the analysis. BMI varied from 14.5 to 55 kg/m(2). Sputum total cell count (mean+/-SD: 12.9 x 10(6) cell/g+/-21.5), eosinophil percent (median; min to max: 0.3%; 0-89.0), and neutrophil percent (mean+/-SD: 63.5+/-26.6%) were within normal limits. Participants with asthma had a higher percentage of sputum eosinophils than those without asthma (P=0.01). However, there was no difference in the total or differential cell counts among the obese and non-obese participants, when the data were analysed according to BMI category, gender, dose of inhaled corticosteroid, and presence or absence of asthma. CONCLUSION: In this large sample of adult asthmatic and non-asthmatic participants, there was no association between BMI and airway inflammation measured by sputum cell counts. Other mechanisms to explain the relationship between obesity and asthma will need to be explored if this association is to be better understood.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it