Mastering Instruments Before Operating on a Patient
Bibliographic record
Abstract
BACKGROUND: We examined the impact of tool complexity on surgeons' performance and evaluated the value of using a simulation-based program for reducing training cost. METHODS: Three pairs of surgical graspers with increasing mechanical complexity, which were designed for open, laparoscopic, and endoscopic procedures, were used in performing a simple object transportation task. Task times and mental workload of 17 surgeons were compared using all 3 variations of the graspers to test the impact of tool complexity on surgical performance. Subsequently, 4 of these surgeons decided to enter a 3-week training phase and practiced with these 3 surgical instruments on a daily basis. Learning curves were plotted to examine the value of using simulation for proficiency training with these tools. RESULTS: Task time was significantly prolonged as tool complexity increased. Practice in a simulated environment shortened the task time significantly and moderately reduced mental workloads. However, the difference in task time varied among the 3 types of tools. Between days 1 and 9, task times for each types of grasper were reduced by 55% (endoscopic), 42% (open), and 22% (laparoscopic). CONCLUSIONS: Tool complexity may degrade a surgeon's performance. Extensive simulation training programs are important for surgeons to gain proficiency in handling a tool before they practice on patients.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".