Canadian Real-World Study Long-Term Clinical Results Using Dupilumab for Chronic Rhinosinusitis With Polyps
Bibliographic record
Abstract
BACKGROUND: Dupilumab, an anti-IL4 receptor-α monoclonal antibody, was the first biologic to be approved in Canada for the treatment of Chronic Rhinosinusitis with Nasal Polyps (CRSwNP). In phase III clinical trials, it has demonstrated to be effective in reducing nasal polyp size and the severity of symptoms, improve disease-specific quality of life, and to have an acceptable safety profile. This study aims to present long-term follow-up data on disease-specific sinonasal outcomes of patients with CRSwNP who have been treated with dupilumab for up to 3 years in a real-world setting. METHODS: Retrospective review of electronic medical records of a single Canadian rhinology center evaluating disease-specific sinonasal outcomes that are routinely collected for clinical care. This study included all patients who received dupilumab for the treatment of CRSwNP and who had completed at least one follow-up visit. The Sino-Nasal Outcome Test (SNOT)-22 was used to evaluate treatment symptom improvement. RESULTS: Ninety-nine patients started dupilumab therapy during the study period. The mean SNOT-22 at the start of therapy was 61.1 (±22.91) At the time of the review, 65 patients had completed 1 year of therapy, 40 had completed 2 years of therapy, and 18 had completed 3 years of therapy. The mean SNOT-22 score at these timepoints was 16.75 (±13.86), 15.02 (±14.40), and 10.22 (±11.56), respectively. CONCLUSION: This real-world study shows that in patients with CRSwNP treated with dupilumab, improvement in disease-specific quality of life seen after 1 year continues and can be maintained at 3 years of treatment.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".