Bibliographic record
Abstract
OBJECTIVE: Laryngology is a rapidly evolving and growing field in Canada. Recent technologies and trends, including endoscopic techniques, KTP and pulsed dye lasers, injection materials, and in-office procedures, are changing the nature and depth of services offered to patients. Our objective was to understand the current state of laryngology practice in Canada. DESIGN: Survey. SETTING: Adult laryngology practices in Canada. METHODS: We identified otolaryngologists working in Canada who self-identified as having a significant laryngology practice. We then asked them to complete an anonymous survey, with questions regarding composition of practice time, endoscopic equipment, electromyography, vocal cord medialization techniques, laser technologies, and plans for the future. MAIN OUTCOME MEASURES: Practice composition and use rates of available technology. RESULTS: Ten of 11 respondents reported that laryngology represents at least 20% of their practice (mean = 53%). All employed rigid laryngoscopy, stroboscopy, and exclusively CO2 laser. Six used distal chip endoscopes, whereas three perform transnasal esophagoscopy. CONCLUSIONS: The survey results demonstrate that laryngology practice in Canada is approaching state of the art and that most laryngologists plan to increase the breadth and scope of their practice as newer technologies become available. Limited funding and national regulations limit the adoption of some cutting-edge techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".