FLS simulator training to proficiency improves laparoscopic performance in the operating room: a randomized controlled trial
Bibliographic record
Abstract
There is growing interest in the use of simulation for surgical skills training and evaluation. The purpose of this study was to assess whether training to proficiency with the FLS laparoscopic simulator would result in improved performance in the operating room (OR). GOALS, a validated tool, was used to measure clinical operating room performance. Nineteen junior residents underwent baseline FLS-testing and GOALS evaluation during elective laparoscopic cholecystectomy. Those with GOALS scores≤15 were randomly assigned to training (n=9) or control (n=8) groups. An FLS proficiency-based curriculum was used in the training group. Scoring on FLS and in the OR was repeated at the end of the study period. Evaluators were blinded to randomization status. Sixteen residents completed the study. There were no differences in baseline simulator or OR scores. After training, simulator scores were higher in the training compared to control group. At the final assessment, the training group improved their OR performance significantly more than the control. The observed improvement was from novice to intermediate level of residency. These results show the transferability of basic laparoscopic skills gained on a physical simulator to the OR and emphasize the value of lapa roscopic simulators for training purposes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".