Development of an objective assessment tool for total laparoscopic hysterectomy: A Delphi method among experts and evaluation on a virtual reality simulator
Bibliographic record
Abstract
INTRODUCTION: Total Laparoscopic hysterectomy (LH) requires an advanced level of operative skills and training. The aim of this study was to develop an objective scale specific for the assessment of technical skills for LH (H-OSATS) and to demonstrate feasibility of use and validity in a virtual reality setting. MATERIAL AND METHODS: The scale was developed using a hierarchical task analysis and a panel of international experts. A Delphi method obtained consensus among experts on relevant steps that should be included into the H-OSATS scale for assessment of operative performances. Feasibility of use and validity of the scale were evaluated by reviewing video recordings of LH performed on a virtual reality laparoscopic simulator. Three groups of operators of different levels of experience were assessed in a Marseille teaching hospital (10 novices, 8 intermediates and 8 experienced surgeons). Correlations with scores obtained using a recognised generic global rating tool (OSATS) were calculated. RESULTS: A total of 76 discrete steps were identified by the hierarchical task analysis. 14 experts completed the two rounds of the Delphi questionnaire. 64 steps reached consensus and were integrated in the scale. During the validation process, median time to rate each video recording was 25 minutes. There was a significant difference between the novice, intermediate and experienced group for total H-OSATS scores (133, 155.9 and 178.25 respectively; p = 0.002). H-OSATS scale demonstrated high inter-rater reliability (intraclass correlation coefficient [ICC] = 0.930; p<0.001) and test retest reliability (ICC = 0.877; p<0.001). High correlations were found between total H-OSATS scores and OSATS scores (rho = 0.928; p<0.001). CONCLUSION: The H-OSATS scale displayed evidence of validity for assessment of technical performances for LH performed on a virtual reality simulator. The implementation of this scale is expected to facilitate deliberate practice. Next steps should focus on evaluating the validity of the scale in the operating room.
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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 | 0.049 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".