Subjective anxiety ratings before and after stressful neurosurgical virtual reality tumor resection task
Bibliographic record
Abstract
Background: The availability of virtual reality (VR) surgical simulators affords the opportunity to assess the influence of stress on neurosurgical operative performance in a controlled laboratory environment. This study sought to examine the effect of a stressful VR neurosurgical task on the subjective anxiety ratings of participants with varying levels of surgical expertise. Methods: Twenty four participants comprised of six staff neurosurgeons, six senior neurosurgical residents (PGY4-6), six junior neurosurgical residents (PGY1-3), and six senior medical students took part in a bimanual VR tumor removal task with a component of sudden uncontrollable intra-operative bleeding. State Trait Anxiety Inventory (STAI) questionnaires were completed immediately pre and post the stress stimulus. The STAI questionnaire consisted of six items (calm, tense, upset, relaxed, content and worried) measured on a Likert scale. Results: Significant increases in subjective anxiety ratings were noted in junior residents (p=0.005) and medical students (p=0.025) while no significant changes were observed for staff and senior neurosurgical residents. Conclusions: Staff and senior residents more effectively mitigate stress compared to junior colleagues in a VR operative environment. Further physiological correlates are needed to determine whether this increased anxiety is paralleled by physiological arousal and altered surgical performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".