Ex Vivo Technical Skills Training Transfers to the Operating Room and Enhances Cognitive Learning
Bibliographic record
Abstract
In Brief Background: Surgical training in the operating room includes acquiring technical skills and cognitive knowledge. Technical skills training on simulated models has been shown to improve technical performance in the operating room, and may also enhance the acquisition of other skills by freeing cognitive capacity. This has yet to be investigated. Methods: We conducted a single-blinded randomized controlled trial to assess the effect of ex vivo technical skills training on cognitive learning in the operating room. Eighteen novice surgical residents were randomized to 2 groups. All participants were taught the basics of fascial closure and performed 1 closure on a low fidelity synthetic model. Residents in the intervention group practiced on the models until technical proficiency was reached. Residents in the control group had no further contact with the models. All residents then performed a fascial closure on a patient in the operating room while listening to a script that contained relevant clinical information. A validated evaluation tool was used to assess the technical merit of the closure. Finally, all participants completed a multiple-choice test designed to test the information retained from the script. Results: The technical performance of the ex vivo trained group was significantly higher than that of the untrained group (P = 0.04). The ex vivo trained group also performed significantly better on the cognitive retention test (P = 0.03). Conclusions: Technical skills training using a low fidelity synthetic simulator resulted in improved technical performance in the operating room, and enhanced the ability of residents to attend to cognitive components of surgical expertise. Technical skills training on inanimate models can improve technical performance in the operating room and may enhance cognitive learning by freeing attention. This has yet to be investigated. We demonstrate that residents who trained on a simulator showed improved technical ability (p = 0.04) and cognitive learning (p = 0.03) in the operating room.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".