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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".