Discrepancies between asthma control criteria in asthmatic patients with and without obesity
Bibliographic record
Abstract
OBJECTIVE: To compare the prevalence of discrepancies between clinical, physiological, and inflammatory asthma control parameters between patients with asthma and obesity and patients with asthma but not obesity using the Asthma Control Scoring System (ACSS). METHODS: A retrospective analysis of demographic data and ACSS scores was performed in two groups of patients with asthma (74 with obesity and 74 without obesity) paired for sex, age, and asthma severity. Scores from each asthma control parameter-clinical (respiratory symptoms), physiological (forced expiratory volume in 1 s), and inflammatory (sputum eosinophil percentage)-were compared. Discrepancy was defined as a >20% difference between two scores. RESULTS: The prevalence of discrepancies between scores was similar between asthma patients with or without obesity. A sub-analysis on patients with uncontrolled asthma (ACSS global score <80%) showed a higher prevalence of discrepancies between the clinical and physiological scores in subjects with obesity, the clinical score being higher than the physiological one in most (87%) cases. CONCLUSIONS: Subjects with obesity and uncontrolled asthma show higher clinical scores than physiological scores, suggesting an under-evaluation of asthma symptoms. Future studies are needed to evaluate the influence of obesity on each type of asthma symptom.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".