Cognitive Load Effect on Intraoperative Learning – A Randomized Trial in Simulation-Based Settings
Bibliographic record
Abstract
BACKGROUND: Surgical trainees in the operating room (OR) face numerous stressful distractions, that increase cognitive load (CL) and potentially hinder learning. While the challenging nature of the OR environment is known, its specific effects on CL and knowledge acquisition in the setting of intraoperative teaching remain underexplored. This study addresses this gap. METHODS: A randomized controlled trial was conducted with 61 medical students at King Saud bin Abdulaziz University for Health Sciences. Participants were divided into a control group (CG), exposed to a low-stress simulation, and an experimental group (EG), subjected to a noisy OR simulation. Both groups participated in a chest tube insertion scenario with intraoperative teaching. CL was measured subjectively using the Modified Multidimensional Cognitive Load Scale (m-MCLS) and objectively via heart rate monitoring and a tactile response task (TRT). Postsimulation quiz scores assessed knowledge acquisition. RESULTS: The EG exhibited significantly higher extraneous CL due to environmental noise (p < 0.001). Objective measures supported this; the EG showed more skipped TRT responses (19% vs. 5.3%, p < 0.001), indicating attentional lapses. Quiz scores were also lower in the EG (57.4% vs. 67.5%, p = 0.02), reflecting impaired knowledge acquisition. Prior meaningful hands-on exposure improved performance on univariate analysis but lost significance after accounting for group assignment. CONCLUSION: Simulating a realistic OR environment intensified cognitive load and trainees impaired knowledge acquisition. Incorporating CL management strategies into surgical training may enhance trainee performance and adaptability in similar real-time encounters. Further research should explore optimal simulation realism levels and interventions to mitigate negative impacts on OR knowledge acquisitions.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".