Implementation of Robotic Telesurgery in Brazil: The First Experimental Remote Surgery Performed Between Two Brazilian Cities
Bibliographic record
Abstract
Background: Telesurgery represents a revolutionary milestone in medicine, allowing surgeons to perform complex procedures at a distance through advanced robotic systems. Although the first telesurgery in Brazil was performed in 2000 with a single-arm robotic platform between São Paulo and Baltimore (USA), no telesurgery had ever been conducted between two distinct Brazilian cities with a state-of-the-art robotic system. The aim is to report the first telesurgery performed between two Brazilian cities, connecting Scolla—Surgical Training Center in Campo Largo and CEONC Hospital in Cascavel, both in the state of Paraná, approximately 600 km apart, using high-performance fiber optic technology with 5G redundancy to perform robotic cholecystectomy in a swine model. Methods: A prospective experimental study was conducted using a 40 kg swine ( Sus scrofa ) as an animal model. Connectivity was established through high-speed fiber optic cable, allowing minimal latency and real-time data transmission. A robotic cholecystectomy was performed remotely, with continuous monitoring of delay parameters and connection quality. Results: Telesurgery was performed without complications, demonstrating the technical feasibility and safety of the procedure between two Brazilian cities. Transmission delays remained within acceptable limits for robotic surgery, and no technical or surgical complications were observed during the procedure. Image quality and responsiveness of robotic commands remained stable throughout the surgery. Conclusion: This study establishes a historic milestone in Brazilian medicine, demonstrating that telesurgery between Brazilian cities is technically feasible and safe. The results open promising perspectives for expanding access to specialized surgical care in remote regions of Brazil, potentially revolutionizing the distribution of medical expertise in the country and Latin America.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".