Cost‐effectiveness Analysis of Inferior Turbinate Reduction and Immunotherapy in Allergic Rhinitis
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis (AR) is a common condition that is frequently associated with atopic inferior turbinate hypertrophy (ITH) resulting in nasal obstruction. Current guidelines support the use of subcutaneous allergen immunotherapy (SCIT) when patients fail pharmacologic management. However, there is a lack of consensus regarding the role of inferior turbinate reduction (ITR), a treatment that we hypothesize is cost-effective compared with other available treatments. METHODS: We conducted a cost-effectiveness analysis comparing the following treatment combinations over a 5-year time horizon for AR patients presenting with atopic nasal obstruction who fail initial pharmacotherapy: (1) continued pharmacotherapy alone, (2) allergy testing and SCIT, (3) allergy testing and SCIT and then ITR for SCIT nonresponders, and (4) ITR and then allergy testing and SCIT for ITR nonresponders. Results were reported as incremental cost-effectiveness ratios (ICERs). RESULTS: For patients who fail initial pharmacotherapy, prioritizing ITR, either by microdebrider-assisting submucous resection or radiofrequency ablation, before SCIT was the most cost-effective strategy. Probabilistic sensitivity analysis demonstrated that prioritizing ITR before SCIT was the most cost-effective option in 95.4% of scenarios. ITR remained cost-effective even with the addition of concurrent septoplasty. CONCLUSION: For many AR patients who present with nasal obstruction secondary to atopic inferior turbinate hypertrophy that is persistent despite pharmacotherapy, ITR is a cost-effective treatment that should be considered prior to immunotherapy. LEVEL OF EVIDENCE: NA - Laryngoscope, 2023 Laryngoscope, 134:1572-1580, 2024.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".