Tele-assistance during neurosurgical education: Remote Education, Augmented Communication, Training and Supervision (REACTS)
Bibliographic record
Abstract
Introduction: Tele-medicine has gained in popularity worldwide, particularly to help offer medical expertise, healthcare delivery and education to developing nations. There is little literature reporting the implementation or analysis of tele-assitance in the setting of surgical education. We have implemented a tele-assistance system, called Remote Education, Augmented Communication, Training and Supervision (REACTS), as a tool to augment mentor-student education in the operating room. This system allows the mentor to observe the student during surgery remotely through screen sharing technology with integrated visual and audio interaction. The goal of this study is to assess the safety and the benefit of REACTS as an educational tool. Methods: Prospective observational study to evaluate the safety and qualitative benefit of REACTS. Results: REACTS was used in 20 cases, including 5 placement of EVDs, 5 pterional craniotomies, 5 Sylvian fissures dissection, 5 lumbar discectomies, and 5 lumbar spine decompressions. No untoward or adverse events were observed. It was judged to be a positive influence on resident and fellow education by the mentors. The main pitfall in its use is to appropriately select the learner for a given procedure. Conclusion: REACTS surgical system is a safe, and a useful adjunct tool for neurosurgical operative education.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".