Prescribing Practices and Barriers of Biologics for Chronic Rhinosinusitis Amongst Otolaryngologists
Bibliographic record
Abstract
OBJECTIVE(S): Biologics for chronic rhinosinusitis with nasal polyposis (CRSwNP) are an evolving therapeutic option, but there are limited data on physician experiences in prescribing them. The goal of this study was to gain a better understanding of these experiences including prescribing practices, patient factors which guide prescriber decision, and physician and patient-reported issues which might limit cost-effectiveness of these therapies. METHODS: A survey was distributed to attending otolaryngologists using the Canadian Society of Otolaryngology (CSOHNS) email distribution and eSurvey program. Responses were tabulated for the entire cohort and compared between rhinologists and non-rhinologists where appropriate. Frequencies and proportions were expressed as a percentage of total respondents. Fisher's exact test was used for statistical analysis between groups. RESULTS: Seventy-nine total survey responses were recorded representing a response rate of 43%. Significantly more rhinologists reported prescribing biologic medications on their own (100% vs. 50%; p < 0.001) and a higher proportion (1 to 10% vs. <1%) of their patients were on biologics compared with non-rhinologists (p = 0.023). Rhinologists were more likely to consider poor response to medical therapies, need for rescue steroids, and comorbid type 2 conditions in their decision to pursue biologics than non-rhinologists, but they also experienced poorer assistance from patient support programs and less availability to medications. CONCLUSION: Rhinologists are more comfortable with prescribing and managing biologics for CRSwNP compared with non-rhinologist colleagues. Clinicians prescribing biologic medications for CRSwNP should be familiar with guidelines, indications, and potential adverse events. LEVEL OF EVIDENCE: N/A Laryngoscope, 134:3493-3498, 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".