Endoscopic polypectomy in the clinic: a pilot cost‐effectiveness analysis
Bibliographic record
Abstract
OBJECTIVE: The purpose of this pilot economic evaluation was to assess the cost-effectiveness of the endoscopic polypectomy in the clinic (EPIC) procedure compared to formal endoscopic sinus surgery (ESS) for the treatment of select chronic rhinosinusitis (CRS) patients with nasal polyposis. DESIGN: Cost-effectiveness analysis using a Markov decision tree model with a 30-year time horizon. The two comparative treatment groups were as follows: (i) EPIC and (ii) ESS. Costs and effects were discounted at a rate of 3.5%. A probabilistic sensitivity analysis was performed. SETTING: Economic perspective of the Canadian government third-party payer. PARTICIPANTS: CRS patients with nasal polyposis who have predominantly isolated symptoms of nasal obstruction with or without olfactory loss. MAIN OUTCOME MEASURES: Incremental cost per quality adjusted life year (QALY). RESULTS: Over a time period of 30 years, the reference case demonstrated that the ESS strategy cost a total of $21,345 and produced 13.17 QALYs while the EPIC strategy cost a total of $5591 and produced 12.93 QALYs. The ESS versus EPIC incremental cost-effectiveness ratio was $65,641/QALY. The probability that EPIC is cost-effective compared to ESS at a maximum willingness-to-pay threshold of $30,000 and $50,000/QALY is 66% and 60%, respectively. CONCLUSIONS: Outcomes from this study have demonstrated that the EPIC procedure may be a cost-effective treatment strategy for 'select' patients with nasal polyposis. Data from this study were obtained from a small pilot trial, and we feel the results warrant a future randomised controlled trial to strengthen the outcomes.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".