[ROBOTIC ORTHOPEDIC SURGERY - WHERE ARE WE STANDING TODAY?]
Bibliographic record
Abstract
INTRODUCTION: The use of robotic and semi-robotic systems in surgery was introduced back in the 1980s, and in orthopedic surgery in the 1990s, but many years passed before it became a significant part of orthopedic surgery. In recent years, robotic surgery, robotic-assisted surgery and advanced technologies have gained popularity and have been integrated as a fundamental part of orthopedic surgery. Adult limb reconstruction, Total Knee Replacement in particular, is probably the highest volume surgery performed in a robotic assisted manner in orthopedic surgery. However, advanced technologies are not limited to knee replacement surgeries. Spine surgery is the second sub-specialty in orthopedics using robotic assistance and navigation in surgery. In recent years we have seen the introduction of advanced technologies into many fields of orthopedic surgery, including foot and ankle surgery, trauma surgery and other subspecialties. In most cases the use of robotic systems is safe, but there are no prospective, long-term high quality studies that indicate a significant advantage for one of the options. There is an abundance of researchers currently investigating this topic. In this article we review the latest uses and developments of robotics and advanced technologies in orthopedic surgery.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".