Instructor Feedback Versus No Instructor Feedback on Performance in a Laparoscopic Virtual Reality Simulator
Bibliographic record
Abstract
OBJECTIVE: To investigate the impact of instructor feedback versus no instructor feedback when training a complex operational task on a laparoscopic virtual reality simulator. BACKGROUND: : Simulators are now widely accepted as a training tool, but there is insufficient knowledge about how much feedback is necessary, which is useful for sustainable implementation. METHODS: A randomized trial complying with CONSORT Statement. All participants had to reach a predefined proficiency level for a complex operational task on a virtual reality simulator. The intervention group received standardized instructor feedback a maximum of 3 times. The control group did not receive instructor feedback. Participants were senior medical students without prior laparoscopic experience (n = 99). Outcome measures were time, repetitions, and performance score to reach a predefined proficiency level. Furthermore, influence of sex and perception of own surgical skills were examined. RESULTS: Time (in minutes) and repetitions were reduced in the intervention group (162 vs 342 minutes; P < 0.005) and (29 vs 65 repetitions; P < 0.005). The control group achieved a higher performance score than the intervention group (57% vs 49%; P = 0.004). Men used less time (in minutes) than women (P = 0.037), but no sex difference was observed for repetitions (P = 0.20). Participants in the intervention group had higher self-perception regarding surgical skills after the trial (P = 0.011). CONCLUSIONS: Instructor feedback increases the efficiency when training a complex operational task on a virtual reality simulator; time and repetitions used to achieve a predefined proficiency level were significantly reduced in the group that received instructor feedback compared with the control group. TRIAL REGISTRATION NUMBER: 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".