Understanding Decision Making In Robotic Surgery: A Knowledge Gap Survey and Cognitive Task Analysis of Robotic Prostatectomy
Bibliographic record
Abstract
Robotic surgery is at the forefront of surgical innovation and presents novel challenges for both postgraduate learners and seasoned specialists. Robotic teaching is underway, often without formalized robotic curricula. Research into robotic surgical steps and surgical decision-making that should be imparted to learners has been neglected. As such, I aimed to determine the knowledge gap of urology residents for a robotic prostatectomy. Further, I also aimed to determine the patterns and cognitive rules used by experienced surgeons to complete a robotic prostatectomy. This master’s thesis included a knowledge gap survey, completed by urology residents, and compared to urologic oncologists, of a robotic prostatectomy and contained both open-ended and rating scale questions. A cognitive task analysis (CTA) was then performed as a series of semi-structured interviews in which incident-probing questions were used to make urologic oncologists explain visual cues and decision-making processes. 42 surveys were administered to urology residents and urologists at The Ottawa Hospital over 10 weeks. There was disagreement between urology resident and urologist responses from the rating scale responses, from the following procedural steps: vesicourethral anastomosis, apical dissection, and seminal vesicle dissection. The open-ended responses found discrepancies between the residents’ and urologists’; understanding of anatomy and surgical decision-making, and of cause-and-consequence cognitive awareness. Subsequently, 16 CTA interviews of four urologic oncologists were completed. After data coding and thematic analysis was performed, CTA grids for each surgeon described a map of a robotic prostatectomy including the steps and goals of the procedure, procedural landmarks, key visual cues for each step, complications and/or error prevention, and management. Specific content not yet described in the literature also includes how the lack of haptic feedback is compensated by robotic surgeons. Additional findings included a gap in urology resident knowledge and understanding of a robotic prostatectomy. The CTA of a robotic prostatectomy documented the surgical decision-making rules, patterns and visual cues urologic oncologists use to avoid errors, and to manage intraoperative surgical complications. This information is key to expanding the understanding of robotic prostatectomy surgical decision-making and training and can be used to produce robust robotic educational curricula.
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 | 0.008 | 0.022 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".