THTP3.4 Performances measures in Robotic-Assisted Surgery- A Systematic Review
Bibliographic record
Abstract
Abstract Background Robotic-Assisted Surgery (RAS) has seen a global rise in the past 30 years. Despite the increased adoption, there is no current standardised training curricula or measure of performance. The aim of this systematic review was to define the RAS metrics used to assess technical performance across the surgical specialities. Methods Using the PRISMA 2020 guidelines, Pubmed, Embase and the Cochrane Library were searched systematically for full texts published 31st January 2020 – 31st January 2022. All randomised controlled trials (RCTs) and observational studies were considered. Review articles and systematic reviews were excluded. The papers’ quality and bias score were assessed using the Newcastle Ottawa Score for the observational studies and Cochrane Risk Tool for the RCTs. Results The initial search yielded 1189 papers of which 74 were eligible. The majority were published in urology (45% n=33). 27 unique metrics were identified and categorised as “task based”, “procedural based”, “cognitive assessments” and “global assessments”. Global assessments were the most common category of assessment (n=12); the most frequently used was GEARs (Global Evaluative Assessment of Robotic Skills). Only 4 metrics (automated, Proficiency Based Progression (PBP), pupillary measures and time to completion) used quantitative measures to assess performance whilst the remaining relied upon Likert scales thereby creating variability amongst users. Conclusion There is wide variation in tools used to assess performance in RAS. The majority of tools are subjective which increases the risk of bias amongst users and therefore accuracy in the measure of performance. A validated, objective global assessment tool that utilises quantitative measures is required.
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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 | 0.026 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".