Could trainees’ finger placement at the surgeon’s console have any effect on the overall outcomes of robotic surgery specifically in radical prostatectomy?
Bibliographic record
Abstract
INTRODUCTION: Robotic surgery for localized prostate cancer offers a greater range of motion attributed to the EndoWrist instruments. Postoperative outcomes are linked to the quality of vesico-urethral anastomosis. Trainees frequently complain of suturing difficulty in a back-handed fashion. We aimed to analyze wrist motion using the DaVinci simulator. We hypothesized that using the thumb and index finger would allow superior surgical proficiency when compared to the middle finger. METHODS: After institutional review board approval, we recruited 42 medical students in one academic medical center. Students were randomly assigned to start with their thumb and index finger (1&2) or thumb and middle finger (1&3). Three standardized modules were used with nine metrics calculated, including: score, total time, economy of motion, efficiency score, collisions, inaccurate puncture, wound approximation, out of view, and penalty subtotal. Statistical analysis of the metrics was calculated using SPSS. RESULTS: Three metrics were found to have differences between the finger placement of 1&3 compared to 1&2. The number of collisions, wound approximation, and penalty score where 1&3 were used had a lower score in each. The number of collisions was 5.7 less in the 1&3 finger placement (p=0.046). This metric was found to have statistically significant differences between finger placement where 1&3 had a lower score compared to 1&2. The wound approximation score was 0.2 points lower when using the 1&3 placement (p=0.075). Lastly, the penalty assigned was 6.5 points lower when using 1&3 (p=0.069). CONCLUSIONS: Although finger placement did not affect the overall score of the completed simulation, instrument collisions and unnecessary wound complications may lead to adverse outcomes when using 1&2 despite offering a wider range of motion. This may be due to decreased comfort in hand position. Trainees may be able to improve the effectiveness of their vesico-urethral anastomosis during robotic-assisted radical prostatectomy.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".