Paranasal sinus disease and sputum eosinophilia in prednisone-dependent asthma.
Bibliographic record
Abstract
OBJECTIVES: to investigate the extent and characteristics of paranasal sinus abnormalities (anatomic and mucosal) on computed tomographic (CT) sinus scans and to determine whether there is a relationship between these findings and eosinophilic airway inflammation in patients with prednisone-dependent asthma. METHODS: we conducted an observational survey of 15 prednisone-dependent asthmatic patients with respect to measures of airway inflammation and CT sinus scans. The pathologic changes on the CT scans were scored using the Lund-Mackay and JAMA staging systems, and several paranasal bony anatomic variations were recorded. Correlations between CT sinus measures and sputum eosinophil count as well as prednisone dose requirement to control sputum eosinophilia were examined. RESULTS: the JAMA and Lund-Mackay staging systems showed that greater sphenoidal mucosal disease was associated with increased prednisone dose requirements (OR 1.7, p = .05; OR 1.6, p = .021). Generally, both staging systems showed that specific sinus site involvement correlated with higher levels of sputum eosinophils. Mucosal thickening in the sphenoid sinus correlated most closely with sputum eosinophilia, followed by the maxillary and ethmoid sinuses and osteomeatal complex. Finally, there appeared to be a limited role for sinus anatomy as a predictive factor for the dose of prednisone required to control sputum eosinophilia. CONCLUSIONS: sinomucosal thickening, but not sinus anatomy, appears to be an important predictor of prednisone requirement and severity of eosinophilic bronchitis in severe asthma.
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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 | 0.000 | 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) | 0.002 | 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".