Bibliographic record
Abstract
Robotic surgical systems for orthopedics are used primarily in procedures to treat osteoarthritis (either partial or full knee replacement or total hip replacement), and in procedures to treat degenerative spinal disease or spinal alignment or curvature abnormalities. Robotic-assisted orthopedic surgeries are intended to improve the accuracy and precision of implant placement and may lead to improved clinical outcomes, shorter recovery time, and fewer revisions. Evidence around their clinical effectiveness is still limited, however, the trend suggests that robot-assisted surgeries may be comparable, or marginally better, in clinical effectiveness when compared to conventional techniques. Robotic-assisted surgery can reduce the length of inpatient stay, but it involves longer operative times. Larger, long-term, randomized controlled trials are needed to confirm their comparative effectiveness. Robotic surgical systems for orthopedics are costly because their initial capital purchase is high, and each procedure requires the use of consumables. However, they may reduce length of stay, which can free up inpatient beds, and reduce rates of revision, which can be cost saving. While some evidence suggests that robotic-assisted knee and hip replacements can be cost-effective, there is limited cost-effectiveness information specific to Canadian contexts. There is a steep learning curve to adopting robotic surgical systems and teams require training and ongoing technical support to ensure that the unit is used to its full capacity. The roles of team members, particularly nursing staff, may change and require advanced technical knowledge. Robotic surgical systems are constantly evolving, integrating new and improved components such as augmented reality, artificial intelligence, digital imaging, and computer-assisted navigation. There will likely be many changes and refinements to these technologies over the coming years.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.141 | 0.095 |
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".