Training, practice, and referral patterns in rhinologic surgery: survey of otolaryngologists.
Bibliographic record
Abstract
OBJECTIVES: Rhinology, which encompasses clinical and surgical treatment of the nasal cavity and paranasal sinuses, is a growing subspecialty with advances in the surgical, clinical, and research realms. The advancement of this subspecialty and its impact on the practice of otolaryngology, in both academic and nonacademic institutions, is not yet understood. METHODS: A novel survey created by our research team was mailed out to 150 randomly selected otolaryngology staff and 8 fellowship-trained rhinologists throughout Canada asking questions related to demographics, training, referral patterns, technique, and adequacy of training. RESULTS: One hundred respondents completed the survey, yielding a response rate of 63%. The average age of rhinologists who responded (38 years) was younger than those who were nonrhinologists (50 years). Compared with fellowship-trained rhinologists, nonrhinologists felt less comfortable with cerebrospinal leak repairs, skull base surgery, frontal sinus surgery, paranasal sinus neoplasm removal, and sphenopalatine artery ligation. CONCLUSIONS: Rhinology is a distinct subspecialty with new fellowship opportunities combined with advances in surgical technique, clinical treatments, and research opportunities. There are procedures that can be performed by both rhinologists and nonrhinologists; however, there is a subset of procedures that nonrhinologists do not feel comfortable performing. These procedures should be referred to fellowship-trained rhinologists who practice out of academic centres.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".