Virtual Reality Simulator for Training on Surgery Ergonomics Skills
Bibliographic record
Abstract
Objective: (1) Background: This paper aims to assess the needs of neurosurgical training in order to strategize the future plans for simulation and rehearsal. (2) Methods: An online questionnaire was conducted among surgeons practicing in different countries across the globe. They were each asked 28 questions regarding their personal profile, rehearsal methods, neurosurgeons’ awareness of virtual reality simulation technology, and the gaps that exist in preoperative neurosurgical training. (3) Results: The questionnaire was filled by 77 respondents with different levels of education: 73.6%, 56 board-certified surgeons, 9.2%,7 senior residents, 7.8%, 6 specialists, and 9.2%, 7 junior residents, and one responder didn’t mention his educational level. There were significant differences in rehearsal methods and surgical teaching methods practiced by the respondents. Among respondents, 90% did believe that virtual reality technology can serve surgical training, and almost all respondents agreed that there is a gap in the existing neurosurgical training in terms of operating room ergonomics. (4) Conclusion: Almost 90% of respondents agreed that patient preparation phases and body positioning training is as important as other surgical skills when compared. All respondents were also in agreement that there needs to be an ergonomics training program in order to decrease musculoskeletal fatigue and injury to themselves and their team members. The findings of this paper indicate that most doctors believe their physical health is affected by operating environment. Adequate education on surgical ergonomics might lead to an improvement in the outcomes for both surgeon and patient.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".