Primary care and upfront computed tomography scanning in the diagnosis of chronic rhinosinusitis: A cost‐based decision analysis
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To diagnose chronic rhinosinusitis (CRS), current guidelines require either endoscopic or computed tomography (CT) findings of sinus disease. To a primary care physician, this means a referral to an otolaryngologist or obtaining a CT scan. Unfortunately, the sensitivity of endoscopy for detecting CRS is low, and examination by the Otolaryngologist may not yield a definitive diagnosis. This leaves CT scanning. However, this is contradicted by recommendations to limit CT scanning for only preoperative planning purposes due to cost concerns. This study aims to provide an evidence-based cost-efficient recommendation for primary care practice. STUDY DESIGN: Health care economics-based decision analysis model. METHODS: A cost-based decision analysis based on literature-reported probabilities and Medicare costs was constructed for two scenarios: 1) primary care physicians who are comfortable initiating first-line treatment for chronic rhinosinusitis, rhinitis, and atypical facial pain; and 2) primary care physicians who are less comfortable with medical management of these conditions. RESULTS: Under both scenarios and the extremes of sensitivity analysis, upfront CT scanning provides cost-efficient diagnosis over presuming a diagnosis of chronic rhinosinusitis. Primary care physicians who attempt first-line treatment can expect $503 (range = $296-$761) saved per patient. Meanwhile, primary care physicians who prefer to refer may expect $326 (range = $299-$353) saved per patient. CONCLUSIONS: In all scenarios, confirming diagnosis with CT scanning prior to treatment or referral is more cost-efficient than presuming a diagnosis of CRS based on symptoms alone.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".