The impact of feedback on laparoscopic skills for surgical residents during COVID-19
Bibliographic record
Abstract
Background: Feedback is a crucial component in skill development, especially for minimally invasive surgery.Our objective was to determine how real-time video verbal feedback compares with delayed written feedback on junior resident performance in laparoscopic skills using at-home laparoscopic training boxes.Methods: Junior surgical residents, training at Memorial University, were randomized into three groups: control group (group A), delayed written feedback group (group B), and live verbal feedback group (group C).Data were collected for a period of 5 months.Participants practiced biweekly on a set of prescribed laparoscopic skills, including peg transfer and intracorporeal knot tying.Intervention groups (groups B and C) received either delayed or live feedback with weekly practice from an expert from the surgical field.Pre-and post-testing were completed.Results: Twelve residents were recruited; one was lost to follow-up.After the data collection period, the average number of pegs transferred correctly increased by 2.8 Æ 1.7 for control group A, 3.0 Æ 2.6 for group B, and 2.0 Æ 1.4 for group C.There was significant group variance as shown by F(2,8) = 5.928, P = 0.026.Post-hoc testing resulted in group B outperforming groups A and C. Groups B and C both improved for the intracorporeal knot-tying task and the number of throws completed; no significant difference was noted between the groups.Qualitative data reported an increase in confidence in completing the tasks at the end of the study for all groups as well as a preference for live verbal feedback versus delayed written feedback.Conclusions: Access to box trainers allowed residents to practice at home, leading to improved skills and confidence.Participants receiving delayed written feedback showed a significant improvement in peg transfer.Further studies with larger sample sizes should be conducted on how feedback, verbal live versus delayed written feedback, can affect resident outcomes in laparoscopic surgery skills.
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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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".