Influence of Otolaryngological Subspecialties on Perception of Transoral Robotic Surgery: An International YO-IFOS Survey
Bibliographic record
Abstract
BACKGROUND: To investigate perception, adoption, and awareness on the part of otolaryngology and head and neck surgeons (OTO-HNS) of transoral robotic surgery (TORS). METHODS: Several items assessed: awareness/perception; access to TORS; training; indications and advantages/hurdles to TORS practice. A subanalysis was performed to assess differences according to the identified otolaryngological subspecialties. RESULTS: A total of 359 people completed the survey. Among subspecialties, while for otolaryngologists 30/359 (8.4%) and H&N surgeons 100/359 (27.9%) TORS plays an effective role in hospital stay, laryngologists frequently disagreed (54.3%). There was a lower incidence among rhinologists and otologists (1.9%). Pediatric surgeons (0.8%) reported a positive response regarding the adoption of robotic surgery, and head and neck specialists expressed an even greater response (14.2%). Low adherence was related to perceived cost-prohibitive TORS, by 50% of H&N surgeons. CONCLUSIONS: Perception, adoption, and knowledge about TORS play a key role in the application of the robotic system, significantly varying across subspecialties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".