Omalizumab Treatment for Severe Atopic Asthma in a Real World Montréal Cohort
Bibliographic record
Abstract
Background: Individuals with severe atopic asthma are poorly controlled with standard treatments, including corticosteroids. A humanized monoclonal antibody binding immunoglobulin E (IgE), omalizumab, is approved to treat patients that are managed poorly despite optimal therapy and that have elevated serum levels of IgE. Objective: The purpose of this study was to determine omalizumab’s effectiveness in a real-world setting. The primary outcome was the number of exacerbations of asthma requiring oral corticosteroid treatment in the 2 year pre-treatment period compared to 2 years post-treatment. The secondary outcome was cumulative dose of prednisone used before and after treatment. Other outcomes that were measured included: reduction in maintenance therapy, change in spirometry (FEV1) data, the stratification of patient population based on smoking status, and average exacerbation number and prednisone use as a function of IgE level and blood eosinophilia count. Methods: Patient data were retrieved (n=41) through the hospital records of patients treated at the Montreal Chest Institute of the McGill University Health Center. Data were gathered and analyzed for the 2 years before the treatment start date and compared to data 2 years after. Results:There was a significant reduction in average exacerbation number from 6.4 pre-treatment to 3.2 post-treatment (p=0.003). There was also a reduction in cumulative prednisone use from 2504mg to 1423mg (p=0.04) following the institution of omalizumab treatment. There was no correlation between either the initial IgE levels and blood eosinophilia and the reduction in exacerbations Conclusion: Omalizumab was effective in reducing exacerbation number and prednisone use for patients with severe refractory asthma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".