Should Oral Corticosteroids be Used in Medical Therapy for Chronic Rhinosinusitis? A Risk Analysis
Bibliographic record
Abstract
OBJECTIVES: Oral corticosteroid (OCS) as a part of appropriate medical therapy (AMT) (formerly maximal medical therapy) in chronic rhinosinusitis remains controversial. While the risks of OCS are well known, the benefit remains unclear due the absence of a standardized prescribing regimen. Consequently, it is difficult to characterize whether the risks of OCS and its ability to avert endoscopic sinus surgery (ESS) are helpful in AMT. When OCS is highly effective at averting surgery, the lesser risks of OCS would be justified because it can avoid the greater risks of ESS. When OCS is poorly effective at averting ESS, the risks of OCS would not be justified because many patients will be exposed to both risks. This study seeks to identify the threshold effectiveness of OCS at averting ESS that would minimize risk exposure to patients. METHODS: A probabilistic risks-based decision analysis was constructed from literature reported incidences and impacts of adverse events of OCS and ESS. Monte Carlo analysis was performed to identify the minimum effectiveness required to avoid further intervention (MERAFI) for chronic sinusitis without nasal polyp (CRSsNP) and chronic sinusitis with nasal polyp (CRSwNP). RESULTS: The analysis showed MERAFI results of 20.8% (95% CI 20.7-20.9%) for CRSsNP and 16.8% (95% CI 16.7-16.9%) for CRSwNP. CONCLUSIONS: Given reported OCS effectiveness in the range of 34-71% in CRSsNP and 46-63% in CRSwNP, this analysis suggests that the inclusion of OCS in AMT may be the lower risk strategy. LEVEL OF EVIDENCE: N/A Laryngoscope, 131:473-481, 2021.
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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 | 0.019 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".