Robotic surgery versus laparoscopy; a comparison between two robotic systems and laparoscopy
Bibliographic record
Abstract
Laparoscopy has found a role in standard urologic practice, and with training programs continuing to increase emphasis on its use, the division between skill sets of established non-laparoscopic urologic practitioners and urology trainees continues to widen. At the other end of the spectrum, as technology progresses apace, advanced laparoscopists continue to question the role of surgical robotics in urologic practice, citing a lack of significant advantage to this modality over conventional laparoscopy. We seek to compare two robotic systems (Zeus and DaVinci) versus conventional laparoscopy in surgical training modules in the drylab environment in the context of varying levels of surgical expertise. A total of 12 volunteers were recruited to the study: four staff, four postgraduate trainees, and four medical student interns. Each volunteer performed repeated time trials of standardized tasks consisting of suturing and knot tying using each of the three platforms: DaVinci, Zeus and conventional laparoscopy. Task times and numbers of errors were recorded for each task. Following each platform trial, a standardized subjective ten-point Likert score questionnaire was distributed to the volunteer regarding various operating parameters experienced including: visualization, fluidity, efficacy, precision, dexterity, tremor, tactile feedback, and coordination. Task translation from laparoscopy to Zeus robotics appeared to be difficult as both suture times and knot-tying times increased in pairwise comparisons across skill levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".