Role of body mass index as a predictor of dupilumab efficacy in patients with severe chronic rhinosinusitis with nasal polyps
Bibliographic record
Abstract
Objective: Response to dupilumab for chronic rhinosinusitis with nasal polyps, albeit almost always excellent, is still not predictable. Our study focuses on the role of body mass index (BMI) on the efficacy of dupilumab. Methods: We present a retrospective multicentre study of 106 patients on dupilumab, stratified in 3 subgroups of BMI. The main therapeutic outcomes investigated were Nasal Polyp Score (NPS), Sino-Nasal-Outcome Test - 22 (SNOT-22), Sniffin' Sticks Identification test and visuo-analogical scale, and the different timing of response, according to De Corso et al. criteria. Results: Dupilumab treatment led to a progressive improvement for all outcomes at all time points. Comparing the different metabolic subgroups, a late response in terms of decrease in NPS was observed only in 3 obese patients. A significant decrease was also found in SNOT-22 score at 6 and 12 months, which was less marked in overweight/obese patients. Conclusions: Our study confirmed the efficacy of dupilumab in each BMI subgroup. However, the efficacy seems to follow different timing with respect to patients' BMI. Our data suggest that patients with a compromised metabolic state present more severe disease at baseline and a possibly delayed response to dupilumab.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".