Ergonomics in Endoscopic Transsphenoidal Surgery: A Survey of the North American Skull Base Society
Bibliographic record
Abstract
Abstract Objective Different surgical set-ups for endoscopic transsphenoidal surgery (ETS) have been described, but studies on their ergonomics are limited. The aim of this article is to describe present trends in the ergonomics of ETS. Design and Participants A 33-question, web-based survey was sent to North American Skull Base Society members in 2018 and 116 responded to it (16% of all members). Most respondents were from North America (76%), in academic practice (87%), and neurosurgeons (65%); they had more than 5 years of experience in ETS (73%), had received specific training (66%), and performed at least 5 procedures/mo (55%). Results Mean reported time for standard and complex procedures were 3.7 and 6.3 hours, respectively. The patient's body is usually positioned in a straight, supine position (84%); the head is in a neutral position (46%) or rotated to the side (38%). Most surgeons perform a binostril technique, work with a partner (95%), and operate standing (94%), holding suction (89%) and dissector (83%); sometimes the endoscope is held by the primary surgeon (22–24%). The second surgeon usually holds the endoscope (72%) and irrigation (42%). During tumor removal most surgeons stand on the same side (65–66%). Many respondents report strain at the dorsolumbar (50%) or cervical (26%) level. Almost one-third of surgeons incorporate a pause during surgery to stretch, and approximately half exercise to be fit for surgery; 16% had sought medical attention for ergonomic-related symptoms. Conclusion Most respondents value ergonomics in ETS. The variability in surgical set-ups and the relatively high report of complaints underline the need for further studies to optimize ergonomics in ETS.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".