Development of a Model for the Acquisition and Assessment of Advanced Laparoscopic Suturing Skills Using an Automated Device
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: 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.
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 | 0.000 |
| 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