Mastering Instruments Before Operating on a Patient
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it