The acceptance of robots in the orthopedic joint replacement operating room
Bibliographic record
Abstract
BACKGROUND: Robotic surgery has seen substantial growth over the years and continues to show promise, with recent implementation into orthopedic surgery. There is limited literature available on patient attitudes and comfort level with robotic compared with conventional surgery. We aimed to develop an understanding of patient views on robot-assisted knee replacement to help the development of patient education materials and facilitate successful implementation. METHODS: A qualitative, descriptive methodology was used. Included participants were those who had undergone total knee replacement in the last 5 years. Participants completed an online semistructured interview assessing their past experiences and their fears and assumptions about robotic surgery. An inductive thematic analysis was completed to organize and present the major themes. RESULTS: Four overarching themes described the areas patients focused on: advancements in surgery, perception of robotic surgery and surgeons, reliability, and patient education materials. Major subthemes included the proven reliability of robots, safety fears, and efficacy. Some participants' fear centred around robot autonomy. Greater comfort with the use of robots would occur if patients were given information about the role of the robot before surgery. CONCLUSION: Patient education materials can help alleviate fears and prevent misperceptions about robot-assisted knee replacement. Materials should include themes of surgical advancements and how surgeons interact with these advancements, level of robot autonomy, and the reliability and safety of the robot.
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 | 0.009 | 0.030 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".