Semiactive robotic-arm system versus patient-specific instrumentation in primary total knee arthroplasty: Efficacy and accuracy
Bibliographic record
Abstract
OBJECTIVE: To compare the difference in efficacy and accuracy during total knee arthroplasty (TKA) among robotic-arm system, patient-specific instrumentation (PSI) and conventional TKA (COTKA). METHODS: Retrospective analysis of 90 advanced knee osteoarthritis (OA) patients in our hospital between June 2019 and December 2020 was conducted. Patients were divided into robotic arm-assisted (RA)TKA (group A), PSITKA (group B) and COTKA (group C), 30 cases in each group. The operation time, intraoperative bleeding, and length of hospital stay were counted. Imaging data of hip-knee-ankle angle (HKA), posterior condylar angle (PCA), lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), and sagittal tibial component angle (sTCA) were statistically analyzed. The postoperative recovery of the patients was evaluated by Knee Society Score (KSS) and the Western Ontario Mac Master University Index Score (WOMAC). RESULTS: Group A had the least intraoperative bleeding. For operation time, group A was the longest compared with group B and group C (P < 0.05), while group B was longer than group C (P < 0.05). There was no significant difference in HKA, LDFA, and MPTA among the three groups, and the lower limb alignments were all restored to the neutral position. PCA of group A and B were both smaller than that of group C and closer to 0° (P < 0.05), but the difference between group A and B was not statistically significant. The sTCA in group A was significantly better than group B, and group B was significantly better than group C (P < 0.05). There were no significant differences in function scores among the three groups. CONCLUSION: Compared to the PSI and CO, RA is more minimally invasive and more accurate in radiographic results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".