Robot-assisted partial knee replacement versus standard total knee replacement (RoboKnees): a protocol for a pilot randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Total knee arthroplasty is a common surgery for end-stage knee osteoarthritis. Partial knee arthroplasty is also a treatment option for patients with arthritis present in only one or two knee compartments. Partial knee arthroplasty can preserve the natural knee biomechanics, but these replacements may not last as long as total knee replacements. Robotic-assisted orthopedic techniques can help facilitate partial knee replacements, increasing accuracy and precision. This trial will investigate the feasibility and assess clinical outcomes for a larger definitive trial. METHODS: This is a protocol for an ongoing parallel randomized pilot trial of 64 patients with uni- or bicompartmental knee arthritis. Patients are randomized to either receive robot-assisted partial knee arthroplasty or manual total knee arthroplasty. The primary outcome of this pilot is investigating the feasibility of a larger trial. Secondary (clinical) outcomes include joint awareness, return to activities, knee function, patient global impression of change, persistent post-surgical pain, re-operations, resource utilization and cost-effectiveness, health-related quality of life, radiographic alignment, knee kinematics during walking gait, and complications up to 24 months post-surgery. DISCUSSION: The RoboKnees pilot study is the first step in determining the outcome of robot-assisted partial knee replacements. Conclusions from this study will be used to design future large-scale trials. This study will inform surgeons about the potential benefits of robot-assisted partial knee replacements. TRIAL REGISTRATION: This study was prospectively registered on clinicaltrials.gov (identifier: NCT04378049) on 4 May 2020, before the first patient was randomized.
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 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".