Utilising an Accelerated Delphi Process to Develop Guidance and Protocols for Telepresence Applications in Remote Robotic Surgery Training
Bibliographic record
Abstract
CONTEXT: The role of robot-assisted surgery continues to expand at a time when trainers and proctors have travel restrictions during the coronavirus disease 2019 (COVID-19) pandemic. OBJECTIVE: To provide guidance on setting up and running an optimised telementoring service that can be integrated into current validated curricula. We define a standardised approach to training candidates in skill acquisition via telepresence technologies. We aim to describe an approach based on the current evidence and available technologies, and define the key elements within optimised telepresence services, by seeking consensus from an expert committee comprising key opinion leaders in training. EVIDENCE ACQUISITION: This project was carried out in phases: a systematic review of the current literature, a teleconference meeting, and then an initial survey were conducted based on the current evidence and expert opinion, and sent to the committee. Twenty-four experts in training, including clinicians, academics, and industry, contributed to the Delphi process. An accelerated Delphi process underwent three rounds and was completed within 72 h. Additions to the second- and third-round surveys were formulated based on the answers and comments from the previous rounds. Consensus opinion was defined as ≥80% agreement. EVIDENCE SYNTHESIS: There was 100% consensus regarding an urgent need for international agreement on guidance for optimised telepresence. Consensus was reached in multiple areas, including (1) infrastructure and functionality; (2) definitions and terminology; (3) protocols for training, communication, and safety issues; and (4) accountability including ethical and legal issues. The resulting formulated guidance showed good internal consistency among experts, with a Cronbach alpha of 0.90. CONCLUSIONS: Using the Delphi methodology, we achieved international consensus among experts for development and content validation of optimised telepresence services for robotic surgery training. This guidance lays the foundation for launching telepresence services in robotic surgery. This guidance will require further validation. PATIENT SUMMARY: Owing to travel restrictions during the coronavirus disease 2019 (COVID-19) pandemic, development of remote training and support via telemedicine is becoming increasingly important. We report a key opinion leader consensus view on a standardised approach to telepresence.
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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 | 0.423 | 0.360 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".