Acute stress decreases bimanual psychomotor performance during resection of simulated brain tumors
Bibliographic record
Abstract
Background: Objective methods to assess the influence of significant stress on neurosurgical bimanual psychomotor performance have not been developed. We utilized NeuroTouch, a virtual reality simulator, to answer two questions: 1) What is the impact of significant stress on bimanual psychomotor performance during the resection of a simulated tumor? 2) Does stress influence performance immediately following the stressful episode? Methods: Uncontrollable ‘intraoperative’ bleeding during one of the tumor resections resulting in simulated patient cardiac arrest served as the acute stressor. Six neurosurgeons, 6 senior and 6 junior neurosurgical residents and 6 senior medical students were studied. The evaluated advanced tier 2 metrics were efficiency index, ultrasonic aspirator path length index, suction coordination index and ultrasonic aspirator bimanual forces ratio. Results: The stress scenario significantly decreased the efficiency index of all groups and significantly decreased performance for many groups for suction coordination index and ultrasonic aspirator path length index. Performance in all advanced tier 2 metrics returned to pre-stress levels in post stress resection scenarios. Conclusions: Our results are consistent with the concept that acute stress initiated by severe intraoperative bleeding significantly decreases bimanual psychomotor performance during the acute episode but had no significant influence on immediate post stress operative 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".