Anti-IL-5 and anti-IL-5 receptor therapy significantly improves quality of life and FEV1 values in patients with severe asthma
Bibliographic record
Abstract
In recent years, the use of monoclonal antibodies directed against interleukin-5 (anti-IL-5) and its receptor alpha (anti-IL-5R) has proven to be an effective therapeutic option for patients with severe asthma by reducing the number of eosinophils, which may promote disease remission. This study aimed to evaluate clinical improvement and remission in patients with severe asthma treated with anti-IL-5 and anti-IL-5R antibodies over a period of 12 months. A cohort study was conducted with 49 patients diagnosed with severe eosinophilic asthma and who did not respond to conventional treatment. During follow-up, medical control was performed every 3 months using spirometry, eosinophil counts, quality of life scales, and disease control. The results revealed an improvement in FEV1 after 3 months of treatment, with statistical significance at 12 months in patients treated with anti-IL-5 and at 9 months in those treated with anti-IL-5R. In addition, better perceptions of asthma control and quality of life were observed, with significant differences at 6 and 12 months. Correlations between spirometry and ACT, ACQ, and AQLQ reflect a progressive recovery of well-being and function. Finally, the remission rate was 41.1% with anti-IL-5 treatment and 47.3% with anti-IL-5R treatment after one year of follow-up. These findings support the efficacy of anti-IL-5 and anti-IL-5R treatment in improving severe asthma control and patients' quality of life, suggesting their key role in disease remission.
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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) | 0.001 | 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) | 0.001 | 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".