Image guidance system use amongst Canadian otolaryngologists: a nationwide survey
Bibliographic record
Abstract
Background The use of image guidance systems has gained widespread acceptance as an adjunctive tool for endoscopic sinus surgery. However, the accessibility and usage of this technology is variable across hospitals in Canada. Study objective The aim of this study is to investigate the availability, usage, and related issues surrounding the use of image guidance systems in endoscopic sinus surgery across Canadian otolaryngology practice settings. Methods An online survey was electronically distributed to practicing otolaryngologists across Canada. The survey contained 27 questions pertaining to the availability, usage, barriers and overall experience of image guidance systems. Results The survey was electronically sent to a total of 654 Canadian otolaryngologists of which 158 responded (response rate 24.2%). Image guidance was available to 56.3% of respondents. Of the respondents without access to IGS, 85.5% indicated they would use it if it was available. Financial (capital cost) was identified as the most important barrier in obtaining IGS by 76.3% of respondents. Conclusion Over half of Canadian otolaryngologists have access to IGS with over 85% of those without access interested in using it if it was made available. A multitude of different factors contribute to this disparity. We hope that the results of this study will help support Canadian otolaryngologists to access IGS. Graphical Abstract
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".