Comparison of Alignment Accuracy and Clinical Outcomes between a <scp>CT</scp>‐Based, Saw Cutting Robotic System and a <scp>CT</scp>‐Free, Jig‐guided Robotic System for Total Knee Arthroplasty
Bibliographic record
Abstract
OBJECTIVE: The different cutting mode of robot-assisted TKAs may influence the accuracy of alignment. The purpose of this study was to compare alignment accuracy and early clinical outcomes between a CT-based, saw cutting robotic system (MAKO) and a CT-free, jig-guided robotic system (ROSA) for total knee arthroplasty (TKA). METHODS: A total of 20 MAKO TKAs and 20 ROSA TKAs from June 2021 to June 2022 were retrospectively analyzed. Differences in the postoperative hip-knee-ankle (HKA) angle, lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), posterior tibial slope (PTS) and 3° outlier frequency of the HKA, LDFA, MPTA and PTS were studied at 3 months and 1 year of follow-up. The operative time and total blood loss (TBL) were compared between these two groups. Clinical outcomes at 1 year after surgery, including range of motion (ROM), Western Ontario McMaster University Osteoarthritis Index (WOMAC) score, and Knee Society Score-2011 (KSS-2011), were also compared between these two groups. RESULTS: The baseline characteristics of the two groups were comparable. There were no significant differences in the mean deviations of postoperative HKA, LDFA, MPTA or PTS between the two groups at 3 months or 1 year (all ps > 0.05). Moreover, there was no significant difference in the percentage of 3° outliers for HKA, LDFA, MPTA, or PTS between the two groups at 3-month or 1-year follow-up (all ps > 0.05). The mean operation time of MAKO was longer than that of ROSA (112.7 ± 12.8 min vs 94.8 ± 23.0 min, p = 0.001), but the mean TBL (1356.7 ± 648.5 mL vs 1384.5 ± 676.3 mL) and transfusion rate (15.0% vs 5.0%) were not significantly different between the two groups (all ps > 0.05). No significant differences were found in postoperative ROM, WOMAC score or KSS score at 1 year (all ps > 0.05). CONCLUSION: The MAKO and ROSA had similar accuracy and precision in TKA alignment. The clinical outcomes at 1 year after surgery were also comparable.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".