Omalizumab therapy: patients who achieve greatest benefit for their asthma experience greatest benefit for rhinitis
Bibliographic record
Abstract
BACKGROUND: Asthma and rhinitis are considered components of a single IgE-mediated inflammatory disorder. However, despite being shown to often co-exist, they are typically treated as independent conditions. Omalizumab, an anti-IgE antibody, has proven effective in the treatment of both asthma and rhinitis. AIMS: To examine whether a response to omalizumab in terms of asthma control predicts a higher likelihood of rhinitis response in patients with concomitant allergic asthma and rhinitis. METHODS: This post hoc analysis was conducted on efficacy results from the SOLAR trial in which patients with moderate-to-severe asthma and rhinitis were randomized to receive omalizumab or placebo for 28 weeks. Patients were classified as asthma responders based on the physician's overall assessment (complete control or marked improvement in a five-level evaluation). Rhinitis responders were identified using the Rhinitis Quality of Life Questionnaire (RQLQ) questionnaire (> or = 1.0 point improvement in overall score). RESULTS: Data were available for 207 omalizumab-treated patients and 192 placebo patients. According to the physicians overall assessment, 123 (59.4%) of omalizumab-treated patients were asthma responders, with the likelihood of a rhinitis response significantly (P < 0.001) greater in these patients than in the placebo group. The odds ratio for rhinitis response in omalizumab-treated asthma responders vs nonresponders was 3.56 (95% CI: 1.94-6.54). CONCLUSIONS: A response in terms of asthma following omalizumab therapy is associated with a significantly increased probability of improvement in rhinitis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".