The first SURS World Congress of Robotic Surgery at Mount Sinai Hospital in New York City: A tribute to the past and the future of robotic urologic surgery
Bibliographic record
Abstract
Technological innovation is a microcosm of human curiosity, intellectual prowess, and perseverance in the face of adversity.The past century and a half have been rife with a series of technologic 'firsts'.From the origins of manned flight in 1903 to the development of the Apollo programme culminating in extraterrestrial travel in 1969, much was achieved during this short period.The pace of innovation in surgery has paralleled that of these other industries.In urology, the advent of robotics has had a profound impact by enabling surgeons to perform procedures with an unprecedented level of precision, enhanced visualization, and decreased invasiveness in comparison to conventional open surgery.Derived from the Czech word 'robota', robot refers to a device capable of doing forced work, and was first referenced to by the author Karel Capek in 1920. 1 Though industrial robots were introduced in the 1960s (e.g., General Motors & the Unimate robot), it was not until decades later that they began to be utilized in medicine in fields including orthopaedic surgery, neurosurgery and urology.[2][3][4] The PROBOT, developed in 1989 in London, was the first urologic robot used clinically to assist in performing transurethral prostatic resections.5 This was followed by the development of a robot at Johns
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.052 | 0.014 |
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".