Anti-Ige monoclonal antibody therapy for the treatment of patients with chronic rhinosinusitis: a multi-disciplinary practice review
Bibliographic record
Abstract
Several treatment options have been described for chronic rhinosinusitis (CRS), yet many patients remain poorly responsive to medical and surgical therapy. Recently, anti-IgE monoclonal antibody has emerged as a potential therapy for CRS. However, to date evidence for its efficacy in this patient population is sparse. The purpose of this study is to evaluate the clinical effect of anti-IgE monoclonal antibody therapy for patients with recalcitrant CRS and asthma treated in a multi-disciplinary clinic. A review of the charts for the 194 patients on anti-IgE monoclonal antibody therapy was performed. 20 patients diagnosed with CRS with poorly controlled disease having failed surgical and/or medical therapy were identified. Data extraction targeted demographic details, asthma, environmental allergy and CRS specific disease related data. For data analysis, for nonparametric data the Mann-Whitney test was used and for binary data Fisher’s exact test was used. Mean age of the cohort was 49 years (range 33-67); eleven patients were male. Mean IgE level was 331.14 IU/ml (57.54-1338.96 IU/ml). Mean treatment duration was 17 (3-71) months. The most common skin prick test positive environmental allergens were dust mite (100%) and cat (65%). 75% of patients had CRS with polyps. Six patients (30%) had AERD. The mean polyp score decreased from 1.8 to 1.0 (p=0.106). Patient olfaction improved in 11 patients (55%) with therapy. Two patients on chronic prednisone treatment were able to discontinue this treatment. None of the patients progressed to require surgical treatment. Anti-IgE monoclonal antibody therapy allowed for clinical CRS disease control in this cohort of patients with severe and recalcitrant CRS. A well-designed clinical trial is needed to further assess the efficacy and safety of this treatment in the CRS population.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".