Economic evaluation of a computed tomography directed referral strategy for chronic rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Chronic rhinosinusitis (CRS) is a prevalent chronic inflammatory disease. The basis of a clinical diagnosis of CRS for primary care physicians (PCPs) is based upon the recognition of a symptom constellation that manifests with the disease. However, because the symptomatology of CRS may overlap with other diagnoses, the referral of patient to the most appropriate specialist may not always occur, leading to further delays in evaluation and treatment. METHODS: Given the emphasis on improving the value of health care in Canada, a decision tree model was designed to evaluate whether an upfront computed tomography (CT) scan of the paranasal sinuses ordered by the PCP for a suspected case of CRS would be more cost-effective when compared to symptom-based specialist referral practice. RESULTS: The CT-based strategy resulted in the patient arriving at the most appropriate specialist 95% (±5%) of the time while the symptom-based referral strategy resulted in the patient arriving at the correct specialist 77% (±18%) of the time. The incremental cost effectiveness ratio (ICER) for the CT-based strategy was $1522 per patient arriving at the correct specialist. CONCLUSION: These results suggest that PCPs can improve the effectiveness of their referrals for CRS by utilising an upfront CT referral strategy. However, it would create an additional cost of approximately $1500 per patient referred. Given these findings, the potential clinical benefits of using an upfront CT scan in the Canadian primary care setting should be further studied to determine the value of the additional money spent to improve the effectiveness of CRS referral.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".