Fluticasone propionate in chronic rhinosinusitis with nasal polyps (CRSwNP): an artificial intelligence-driven consensus
Bibliographic record
Abstract
Introduction: Fluticasone propionate (FP) is a topical corticosteroid used to treat rhinosinusitis with nasal polyposis (CRSwNP). However, the need for a consensus on its use stems from the increasing focus on optimizing topical therapies to improve clinical outcomes and minimize systemic side effects. Materials and methods: The Butterfly Decisions AI platform facilitated the collection and integration of evaluations and feedback, facilitating an expert consensus on 13 statements. Results: The participants agreed highly on the different statements. The experts agreed that FP effectively reduces the need for surgery and controls the symptoms of CRSwNP. The use of advanced delivery systems significantly improved drug delivery and therapeutic outcomes. Treatment with FP was associated with a reduction in the recurrence of nasal polyps and an improvement in the patient's quality of life. Conclusions: FP, as other equal corticosteroids, represents a first-line local therapy for patients with CRSwNP without complicating comorbidities due to its high efficacy and low systemic bioavailability. The Butterfly Decisions platform has demonstrated the effectiveness of integrating AI tools into clinical decision-making, improving the transparency and objectivity of assessments.
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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 | 0.065 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".