Performance in the Fundamentals of Laparoscopic Surgery: Does it reflect global rating scales in the Objective Structured Assessment of Technical Skills in porcine laparoscopic surgery?
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
To correlate the utility of the Fundamentals of Laparoscopic Surgery (FLS) manual skills program with the Objective Structured Assessment of Technical Skills (OSATS) global rating scale in evaluating operative performance. The Asian Urological Surgery Training and Educational Group (AUSTEG) Laparoscopic Upper Tract Surgery Course (LUTSC) implemented and validated the FLS program for its usage in laparoscopic surgical training. Delegates’ basic laparoscopic skills were assessed using three different training models (peg transfer, precision cutting, and intra-corporeal suturing). They also performed live porcine laparoscopic surgery at the same workshop. Live surgery skills were assessed by blinded faculty using the OSATS rating scale. From March 2016 to March 2019, a total of 81 certified urologists participated in the course, with a median of 5 years’ experience post-residency. Although differences in task time did not reach statistical significance, those with more surgical experience were visibly faster at completing the peg transfer and intra-corporeal suturing FLS tasks. However, they took longer to complete the precision cutting task than participants with less experience. Overall OSATS scores correlated weakly with all three FLS tasks (peg transfer time: r = −0.331, r2 = 0.110; precision cutting time: r = −0.240, r2 = 0.058; suturing with intra-corporeal knot time: r = −0.451, r2 = 0.203). FLS task parameters did not correlate strongly with OSATS globing rating scale performance. Although the FLS task models demonstrated strong validity, it is important to assimilate the inconsistencies when benchmarking technical proficiency against real-life operative competence, as evaluated by FLS and OSATS respectively.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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