How Surgeons Think to Avoid Error: A Case Study of the Neurovascular Bundle Sparing During a Robotic Prostatectomy
Bibliographic record
Abstract
OBJECTIVE: To illustrate how experts efficiently navigate a "slowing down moment" to obtain optimal surgical outcomes using the neurovascular bundle sparing during a robotic prostatectomy as a case study. DESIGN: A series of semistructured interviews with four expert uro-oncologists were completed using a cognitive task analysis methodology. Cognitive task analysis, CTA, refers to the interview and extraction of a general body of knowledge. Each interview participant completed four 1 to 2-hour semistructured CTA interviews. The interview data were then deconstructed, coded, and analyzed using a grounded theory analysis to produce a CTA-grid for a robotic prostatectomy for each surgeon, with headings of: surgical steps, simplification maneuvers, visual cues, error/complication recognition, and error/complication management and avoidance. SETTING: The study took place at an academic teaching hospital located in an urban center in Canada. PARTICIPANTS: Four expert uro-oncologists participated in the study. RESULTS: Visual cues, landmarks, common pitfalls, and technique were identified as the 4 key components of the decision-making happening during a slowing down moment in the neurovascular bundle sparing during a robotic prostatectomy. CONCLUSION: The data obtained from the CTA is novel information identifying patterns and cues that expert surgeons use to inform their surgical decision-making and avoid errors. This decision-making knowledge of visual cues, landmarks, common pitfalls and techniques is also generalizable for other surgical subspecialties. Surgeon educators, surgical teaching programs and trainees looking to improve their decision-making skills could use these components to guide their educational strategies.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".