Cost‐effectiveness of Endoscopic Polypectomy in Clinic compared to Endoscopic Sinus Surgery: A modelling study
Bibliographic record
Abstract
OBJECTIVE: To compare costs and quality-adjusted life years of patients receiving endoscopic polypectomy in the clinic those receiving endoscopic sinus surgery from the perspective of Canada's health system. METHOD: We developed a Markov model to simulate costs and quality-adjusted life years among a cohort of patients with chronic rhinosinusitis with polyps who received endoscopic polypectomy in clinic or endoscopic sinus surgery, over a patient's lifetime. Our study focused on patients with chronic rhinosinusitis with polyps who have the predominant symptom of bilateral nasal obstruction. Cost data were obtained from Canadian sources. Health utility values were derived from Sino-nasal Outcome Test (SNOT-22) scores reported in the published literature. A cycle length of six months was used. Both costs and quality-adjusted life years were discounted using an annual rate of 1.5%. We conducted one-way and probabilistic sensitivity analyses to assess the robustness of the study findings. RESULTS: Endoscopic polypectomy in clinic was a cost-saving option as it had a lower cost (C$736 vs. C$6,728) and produced more quality-adjusted life years (13.85 vs 13.06) compared to endoscopic sinus surgery. Our findings were, however, highly sensitive to health utility values. Results from probabilistic sensitivity analysis showed that endoscopic polypectomy in clinic had a lower cost and was more effective compared to endoscopic sinus surgery in 99.86% of iterations. CONCLUSION: From the perspective of the publicly funded healthcare system, endoscopic polypectomy in clinic was economically attractive compared to endoscopic sinus surgery for chronic rhinosinusitis with polyps patients who have a primary symptom of bilateral nasal obstruction. The cost-effectiveness results are, however, highly dependent on the relative impact of endoscopic polypectomy in the clinic and endoscopic sinus surgery on post-surgery health utility values.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".