Development of a Model for the Acquisition and Assessment of Advanced Laparoscopic Suturing Skills Using an Automated Device
Bibliographic record
Abstract
BACKGROUND: device. METHODS: Experienced (ES) and novice surgeons (NS) performed continuous suturing after watching an instructional video. Scores were based on time and accuracy, and Global Operative Assessment of Laparoscopic Surgery. Data are shown as medians [25th-75th percentiles] (ES vs NS). Interrater reliability was calculated using intraclass correlation coefficients (confidence interval). RESULTS: Seventeen participants were enrolled. Experienced surgeons had significantly greater task (980 [964-999] vs 666 [391-711], P = .0035) and Global Operative Assessment of Laparoscopic Surgery scores (25 [24-25] vs 14 [12-17], P = .0029). Interrater reliability for time and accuracy were 1.0 and 0.9 (0.74-0.96), respectively. All experienced surgeons agreed that the task was relevant to practice. CONCLUSION: This study provides validity evidence for the task as a measure of laparoscopic suturing skill using an automated suturing device. It could help trainees acquire the skills they need to better prepare for clinical learning.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".