New technology‐based assistive techniques in total knee arthroplasty: A Bayesian network meta‐analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: The radiological and clinical efficiency among robot-assisted surgery (RAS), computer-assisted navigation system (CAS) and conventional (CON) total knee arthroplasty (TKA) remains controversial. METHODS: Bayesian network meta-analysis (NMA) and systematic review were performed to investigate radiological and clinical efficiency respectively. The certainty of the evidence was evaluated using GRADE and CERQual tool. RESULTS: Thirty-four RCTs (7289 patients and 7424 knees) were included. The NMA showed that RAS-TKA had the highest probability for mechanical axis restoration (odds ratio for RAS vs. CAS 3.79, CrI 1.14 to 20.54, very low certainty), followed by CAS-TKA (odds ratio for CAS vs. CON 2.55, CrI 1.67 to 4.01, very low certainty) and then CON-TKA, without significant differences in other radiological parameters. No differences were found in clinical outcomes after qualitative systematic review (overall low certainty). CONCLUSIONS: Technology-based assistive techniques (CAS and RAS) may surpass the CON-TKA, when considering higher radiological accuracy and comparable clinical outcomes. This article is protected by copyright. All rights reserved.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".