The use of computer‐assisted orthopedic surgery for total knee replacement in daily practice: a survey among ESSKA/SGO‐SSO members
Bibliographic record
Abstract
Computer-assisted orthopedic surgery (CAOS) for total knee arthroplasty is an emerging surgical tool, yet little is known about how it is being used in everyday orthopedic centers. We sought to better understand physicians' current practices and beliefs on this topic through performing a Web-based survey. Between December 2006 and January 2007, a 24-question survey was emailed to 3,330 members of the European Society of Sports Traumatology Knee Surgery and Arthroscopy (ESSKA) and the Swiss Orthopedic Society (SGO-SSO), with 389 (11.7%) agreeing to participate. Of this group, 202 (51.9%) reported that their center was equipped with a navigation system, which was an image-free based system for most (83.2%) and was primarily used for total knee arthroplasty (61.4%). In terms of the proportion of use, 50.5% of respondents used their navigation system in less than 25% of cases, 16.3% in 25-50% of cases, 7.4% in 51-75% of cases, and 25.7% in more than 75% of cases. The potential for improving the alignment of prosthesis was the most strongly cited reason for using a navigation system, while the potential for increasing operation times and the risk of infections were the most strongly cited reasons for not using a navigation system. Approximately half of respondents surveyed believed navigation systems were a real innovation contributing to the improvement of total knee implantation. However, heavy usage of computer-assisted navigation (> or =51% of cases) was observed in only 33.1% of respondents, with only a quarter using it at rates that could be considered frequent (>75% of cases). Forty-eight percent of respondents said they will use a navigation system in more cases and 39.1% that their usage will stay the same. These findings indicate that CAOS is being used only moderately in current practices, though respondents generally had a positive opinion of its potential benefits. Physicians may be awaiting more data before adopting the use of these systems, though survey responses also suggest a projected increase in their use in the coming years.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".