Incidence and severity of Visually Induced motion Sickness during 3D laparoscopy In Operators who had No experience with it (VISION).
Bibliographic record
Abstract
AIM: The aim of the study was to evaluate the incidence and severity of visually induced motion sickness (VIMS) during 3D laparoscopy, in operators without prior experience. MATERIAL AND METHODS: Design: A retrospective comparative study (Canadian Task Force classification II-2). Setting: A university hospital. Intervention: Gynecologic surgery. Main outcome measure: This is a prospective observational study, which enrolled 9 surgeons as participants. None of these surgeons had any prior experience with 3D laparoscopy. Each participant performed 10 consecutive cases of 3D laparoscopy in patients with benign or premalignant gynecological diseases. The primary outcome measure was the incidence and severity of VIMS, which was evaluated using the validated Simulator Sickness Questionnaire. Personal preferences, discomfort, and ease of 3D laparoscopy were also evaluated. RESULTS: Sixty-seven percent of surgeons experienced VIMS during their first 3D laparoscopy case. The incidence and severity of VIMS dramatically decreased from the second case onward. However, in some surgeons (22-44%), VIMS did not completely disappear until the tenth case. With respect to the discomfort using 3D laparoscopy, 84 self-reported responses after each surgery were "favor 3D laparoscopy," and "no" in 61 (72.6%) and 47 (55.9%) participants, respectively. Most participants found it easier to perform 3D laparoscopy than 2D laparoscopy. CONCLUSIONS: The occurrence of visually induced symptoms in susceptible individuals during 3D laparoscopy is high, particularly during their first case. This suggests the need for increasing surgeons' awareness regarding the possibility of discomfort.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".