Design requirements to improve laparoscopy via XR
Bibliographic record
Abstract
Laparoscopic surgery has the advantage of avoiding large open in-cisions and thereby decreasing blood loss, pain, and discomfort to patients. However, on the other side, it is hampered by restricted workspace, ambiguous communication, and surgeon fatigue caused by non-ergonomic head positioning. We aimed to identify critical problems and suggest design requirements and solutions. We used user and task analysis methods to learn about practices performed in an operating room by observing surgeons in their working environment to understand how they performed tasks and achieved their intended goals. Drawing on observations and analysis from recorded laparoscopic surgeries, we have identified several constraints and design requirements to propose potential solutions to address the issues. Surgeons operate in a dimly lit environment, surrounded by monitors, and communicate through verbal commands and pointing gestures. Therefore, performing user and task analysis allowed us to understand the existing problems in laparoscopy better while identifying several communication constraints and design requirements, which a solution has to follow to address those problems. Our contributions include identifying design requirements for laparoscopy surgery through a user and task analysis. These requirements propose design solutions towards improved surgeons' comfort and make the surgical procedure less laborious.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".