Instructor feedback versus no instructor feedback on performance in a laparoscopic virtual reality simulator: a randomized educational trial
Bibliographic record
Abstract
BACKGROUND: Several studies have found a positive effect on the learning curve as well as the improvement of basic psychomotor skills in the operating room after virtual reality training. Despite this, the majority of surgical and gynecological departments encounter hurdles when implementing this form of training. This is mainly due to lack of knowledge concerning the time and human resources needed to train novice surgeons to an adequate level. The purpose of this trial is to investigate the impact of instructor feedback regarding time, repetitions and self-perception when training complex operational tasks on a virtual reality simulator. METHODS/DESIGN: The study population consists of medical students on their 4th to 6th year without prior laparoscopic experience. The study is conducted in a skills laboratory at a centralized university hospital. Based on a sample size estimation 98 participants will be randomized to an intervention group or a control group. Both groups have to achieve a predefined proficiency level when conducting a laparoscopic salpingectomy using a surgical virtual reality simulator. The intervention group receives standardized instructor feedback of 10 to 12 min a maximum of three times. The control group receives no instructor feedback. Both groups receive the automated feedback generated by the virtual reality simulator. The study follows the CONSORT Statement for randomized trials. Main outcome measures are time and repetitions to reach the predefined proficiency level on the simulator. We include focus on potential sex differences, computer gaming experience and self-perception. DISCUSSION: The findings will contribute to a better understanding of optimal training methods in surgical education. TRIAL REGISTRATION: NCT01497782.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".