Characteristics of asthma patients responding to anti-IgE therapy
Bibliographic record
Abstract
Introduction: Omalizumab is recommended in the treatment of patients with refractory asthma; response to this expensive therapy is not uniform. The characteristics associated with good treatment response are poorly defined. Objectives: Our goal was to determine what patient characteristics, other than atopy and IgE level, might predict response to therapy with omalizumab. Methods : We carried out a retrospective review of adult patients, followed at a tertiary asthma clinic, who received treatment with omalizumab for at least 6 months. The following data were collected: IgE level, atopy, sputum eosinophils and neutrophils, blood eosinophils, associated rhinosinusitis / nasal polyps. Response to therapy was judged by one asthma specialist and an asthma nurse clinician based on maintenance therapy required, exacerbations, FEV1 and ACQ score. Groups were compared using Chi-square and t-tests. Results: 17 patients were included; 12 were considered to have responded to omalizumab. Of 13 patients who provided induced sputum for inflammatory profile, 9 were classified as responders. All responders had elevated sputum eosinophils (> 2%) vs 25% of non-responders (p=0.014) but none had high neutrophils (>65%) vs all non-responders (p= 0.001). Elevated blood eosinophilia (> 0.5 x10^9/L) was present in 82% of responders while 80% of non-reponders had results within normal limits (p= 0.036). Conclusions: Sputum inflammatory profile and blood eosinophils predict response to omalizumab among atopic patients with refractory 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".