Canadian real-world study of access and clinical results using dupilumab for chronic rhinosinusitis with polyps
Bibliographic record
Abstract
Abstract Background Dupilumab is the first monoclonal antibody therapy to be approved in Canada for the treatment of Chronic Rhinosinusitis with Nasal Polyps (CRSwNP). The goal of the study was to assess its effectiveness and efficacy in a real-world setting. This study aims to assess how clinical outcomes of biologic therapy in real-world application (effectiveness) correspond to outcomes in clinical trials (efficacy) and to look into factors that might explain an efficacy-effectiveness gap. Methods A retrospective study evaluating disease specific sinonasal outcomes routinely collected for clinical care. This study included patients who were evaluated for coverage of dupilumab at a tertiary care rhinology clinic for the treatment of CRSwNP in the first year since dupilumab was approved in Canada for this indication. Sinonasal outcomes were be evaluated by collecting data on the Sino-Nasal Outcome Test (SNOT)-22 questionnaire. Results Eighty-five patients were considered for dupilumab therapy during the study period, 49% patients were able to attain coverage for the requested therapy. The mean SNOT-22 score at baseline was 60.56 (SD 21.63). After 16 weeks of treatment the mean SNOT-22 score decreased by 37 points to 23.36; at 28 weeks the mean SNOT-22 was 23.47. After 1 year, the mean SNOT-22 score was 14.37. Conclusion Patients treated with dupilumab for CRSwNP at out tertiary rhinology clinic showed substantial clinical symptom improvement that is similar to that observed in prior randomised clinical trials. No serious adverse effects related to dupilumab were reported in this cohort. Long-term follow-up is needed to inform effectiveness analyses beyond the 1 year clinical trial duration. Graphical Abstract
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".