Switching biologics in chronic rhinosinusitis with nasal polyps: A multicenter Canadian experience
Bibliographic record
Abstract
BACKGROUND: Type 2 biologics have been used increasingly for the treatment of chronic rhinosinusitis with nasal polyps (CRSwNP). However, patterns of biologic switching are understudied, and established guidelines for sequential or simultaneous use do not yet exist. METHODS: This is a Canadian multicenter retrospective study of real-world patient data. Patients were included if they had recurrent CRSwNP despite maximal medical and surgical management, and received at least one dose of a type 2 biologic. Patients who remained on their initial biologic comprised the continuous group. Patients with sequential or simultaneous use of more than one biologic comprised the switched group. We compared the characteristics of patients who continued and switched biologics. RESULTS: Note that 225 consecutive patients were included. Thirty-six (16%) switched biologics at least once, and six (3%) switched twice. The most common switch was from mepolizumab to dupilumab, with poor control of CRSwNP symptoms being the leading cause for this switch. Lack of efficacy was the main reason for switching off mepolizumab and omalizumab, while adverse events were the leading cause for switching off dupilumab. Additionally, mepolizumab patients were more likely to switch biologics late in their treatment, while dupilumab patients rarely switched after 12 months of therapy (p-value < 0.001). CONCLUSIONS: Switching biologics for CRSwNP is frequent in Canadian rhinology practices, with 16% of patients switching at least once. The most common switch is from mepolizumab to dupilumab with inadequate CRSwNP control driving this switch. This study may help guide sequential or simultaneous use of biologics in CRSwNP patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".