Current Feasibility of Urologic Telesurgery in Low/Middle Income Countries
Bibliographic record
Abstract
It is estimated that nearly five billion people do not have access to surgical care. Approximately 94% of individuals in low- and middle-income countries (LMICs) lack access to surgery in comparison to 14.9% in high-income countries (HICs). There are several urologic conditions requiring surgical intervention that are not treated because of the limited number of expert urologists in LMICs. Telesurgery is a concept that connects patients and surgeons in different locations through the use of a robotic surgery system. In this review, we explain the origins of telesurgery as well as the benefits and obstacles to its global implementation. Telesurgery can reduce travel times and the dangers associated with traveling for surgical care in LMICs. Additionally, telesurgery allows patients in LMICs to gain access to expert urologists while also providing effective training to upcoming surgeons. However, LMICs require substantial investment to improve digital infrastructure that will support urologic telesurgery. There will also be ethical, legal, and policy considerations that will need to be resolved for safe and equitable urologic telesurgery to occur. There have been multiple successful applications of urologic telesurgery, suggesting that the technology for this to become routine is already available. The time for international collaboration must begin now to reduce global disparities in access to urologic surgery.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".